Publications

Displaying 1 - 91 of 91
  • Alhama, R. G., Rowland, C. F., & Kidd, E. (2020). Evaluating word embeddings for language acquisition. In E. Chersoni, C. Jacobs, Y. Oseki, L. Prévot, & E. Santus (Eds.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 38-42). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL). doi:10.18653/v1/2020.cmcl-1.4.

    Abstract

    Continuous vector word representations (or
    word embeddings) have shown success in cap-turing semantic relations between words, as evidenced by evaluation against behavioral data of adult performance on semantic tasks (Pereira et al., 2016). Adult semantic knowl-edge is the endpoint of a language acquisition process; thus, a relevant question is whether these models can also capture emerging word
    representations of young language learners. However, the data for children’s semantic knowledge across development is scarce. In this paper, we propose to bridge this gap by using Age of Acquisition norms to evaluate word embeddings learnt from child-directed input. We present two methods that evaluate word embeddings in terms of (a) the semantic neighbourhood density of learnt words, and (b) con-
    vergence to adult word associations. We apply our methods to bag-of-words models, and find that (1) children acquire words with fewer semantic neighbours earlier, and (2) young learners only attend to very local context. These findings provide converging evidence for validity of our methods in understanding the prerequisite features for a distributional model of word learning.
  • Anastasopoulos, A., Lekakou, M., Quer, J., Zimianiti, E., DeBenedetto, J., & Chiang, D. (2018). Part-of-speech tagging on an endangered language: a parallel Griko-Italian Resource. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018) (pp. 2529-2539).

    Abstract

    Most work on part-of-speech (POS) tagging is focused on high resource languages, or examines low-resource and active learning settings through simulated studies. We evaluate POS tagging techniques on an actual endangered language, Griko. We present a resource that contains 114 narratives in Griko, along with sentence-level translations in Italian, and provides gold annotations for the test set. Based on a previously collected small corpus, we investigate several traditional methods, as well as methods that take advantage of monolingual data or project cross-lingual POS tags. We show that the combination of a semi-supervised method with cross-lingual transfer is more appropriate for this extremely challenging setting, with the best tagger achieving an accuracy of 72.9%. With an applied active learning scheme, which we use to collect sentence-level annotations over the test set, we achieve improvements of more than 21 percentage points
  • Asano, Y., Yuan, C., Grohe, A.-K., Weber, A., Antoniou, M., & Cutler, A. (2020). Uptalk interpretation as a function of listening experience. In N. Minematsu, M. Kondo, T. Arai, & R. Hayashi (Eds.), Proceedings of Speech Prosody 2020 (pp. 735-739). Tokyo: ISCA. doi:10.21437/SpeechProsody.2020-150.

    Abstract

    The term “uptalk” describes utterance-final pitch rises that carry no sentence-structural information. Uptalk is usually dialectal or sociolectal, and Australian English (AusEng) is particularly known for this attribute. We ask here whether experience with an uptalk variety affects listeners’ ability to categorise rising pitch contours on the basis of the timing and height of their onset and offset. Listeners were two groups of English-speakers (AusEng, and American English), and three groups of listeners with L2 English: one group with Mandarin as L1 and experience of listening to AusEng, one with German as L1 and experience of listening to AusEng, and one with German as L1 but no AusEng experience. They heard nouns (e.g. flower, piano) in the framework “Got a NOUN”, each ending with a pitch rise artificially manipulated on three contrasts: low vs. high rise onset, low vs. high rise offset and early vs. late rise onset. Their task was to categorise the tokens as “question” or “statement”, and we analysed the effect of the pitch contrasts on their judgements. Only the native AusEng listeners were able to use the pitch contrasts systematically in making these categorisations.
  • Bentz, C., Dediu, D., Verkerk, A., & Jäger, G. (2018). Language family trees reflect geography and demography beyond neutral drift. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 38-40). Toruń, Poland: NCU Press. doi:10.12775/3991-1.006.
  • De Boer, B., Thompson, B., Ravignani, A., & Boeckx, C. (2020). Analysis of mutation and fixation for language. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 56-58). Nijmegen: The Evolution of Language Conferences.
  • Bowerman, M., & Perdue, C. (Eds.). (1990). The structure of the simple clause in language acquisition [Special Issue]. Linguistics, 28(6).
  • Brand, J., Monaghan, P., & Walker, P. (2018). Changing Signs: Testing How Sound-Symbolism Supports Early Word Learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1398-1403). Austin, TX: Cognitive Science Society.

    Abstract

    Learning a language involves learning how to map specific forms onto their associated meanings. Such mappings can utilise arbitrariness and non-arbitrariness, yet, our understanding of how these two systems operate at different stages of vocabulary development is still not fully understood. The Sound-Symbolism Bootstrapping Hypothesis (SSBH) proposes that sound-symbolism is essential for word learning to commence, but empirical evidence of exactly how sound-symbolism influences language learning is still sparse. It may be the case that sound-symbolism supports acquisition of categories of meaning, or that it enables acquisition of individualized word meanings. In two Experiments where participants learned form-meaning mappings from either sound-symbolic or arbitrary languages, we demonstrate the changing roles of sound-symbolism and arbitrariness for different vocabulary sizes, showing that sound-symbolism provides an advantage for learning of broad categories, which may then transfer to support learning individual words, whereas an arbitrary language impedes acquisition of categories of sound to meaning.
  • Butterfield, S., & Cutler, A. (1990). Intonational cues to word boundaries in clear speech? In Proceedings of the Institute of Acoustics: Vol 12, part 10 (pp. 87-94). St. Albans, Herts.: Institute of Acoustics.
  • Byun, K.-S., De Vos, C., Roberts, S. G., & Levinson, S. C. (2018). Interactive sequences modulate the selection of expressive forms in cross-signing. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 67-69). Toruń, Poland: NCU Press. doi:10.12775/3991-1.012.
  • Cristia, A., Ganesh, S., Casillas, M., & Ganapathy, S. (2018). Talker diarization in the wild: The case of child-centered daylong audio-recordings. In Proceedings of Interspeech 2018 (pp. 2583-2587). doi:10.21437/Interspeech.2018-2078.

    Abstract

    Speaker diarization (answering 'who spoke when') is a widely researched subject within speech technology. Numerous experiments have been run on datasets built from broadcast news, meeting data, and call centers—the task sometimes appears close to being solved. Much less work has begun to tackle the hardest diarization task of all: spontaneous conversations in real-world settings. Such diarization would be particularly useful for studies of language acquisition, where researchers investigate the speech children produce and hear in their daily lives. In this paper, we study audio gathered with a recorder worn by small children as they went about their normal days. As a result, each child was exposed to different acoustic environments with a multitude of background noises and a varying number of adults and peers. The inconsistency of speech and noise within and across samples poses a challenging task for speaker diarization systems, which we tackled via retraining and data augmentation techniques. We further studied sources of structured variation across raw audio files, including the impact of speaker type distribution, proportion of speech from children, and child age on diarization performance. We discuss the extent to which these findings might generalize to other samples of speech in the wild.
  • Ip, M. H. K., & Cutler, A. (2018). Asymmetric efficiency of juncture perception in L1 and L2. In K. Klessa, J. Bachan, A. Wagner, M. Karpiński, & D. Śledziński (Eds.), Proceedings of Speech Prosody 2018 (pp. 289-296). Baixas, France: ISCA. doi:10.21437/SpeechProsody.2018-59.

    Abstract

    In two experiments, Mandarin listeners resolved potential syntactic ambiguities in spoken utterances in (a) their native language (L1) and (b) English which they had learned as a second language (L2). A new disambiguation task was used, requiring speeded responses to select the correct meaning for structurally ambiguous sentences. Importantly, the ambiguities used in the study are identical in Mandarin and in English, and production data show that prosodic disambiguation of this type of ambiguity is also realised very similarly in the two languages. The perceptual results here showed however that listeners’ response patterns differed for L1 and L2, although there was a significant increase in similarity between the two response patterns with increasing exposure to the L2. Thus identical ambiguity and comparable disambiguation patterns in L1 and L2 do not lead to immediate application of the appropriate L1 listening strategy to L2; instead, it appears that such a strategy may have to be learned anew for the L2.
  • Ip, M. H. K., & Cutler, A. (2018). Cue equivalence in prosodic entrainment for focus detection. In J. Epps, J. Wolfe, J. Smith, & C. Jones (Eds.), Proceedings of the 17th Australasian International Conference on Speech Science and Technology (pp. 153-156).

    Abstract

    Using a phoneme detection task, the present series of
    experiments examines whether listeners can entrain to
    different combinations of prosodic cues to predict where focus
    will fall in an utterance. The stimuli were recorded by four
    female native speakers of Australian English who happened to
    have used different prosodic cues to produce sentences with
    prosodic focus: a combination of duration cues, mean and
    maximum F0, F0 range, and longer pre-target interval before
    the focused word onset, only mean F0 cues, only pre-target
    interval, and only duration cues. Results revealed that listeners
    can entrain in almost every condition except for where
    duration was the only reliable cue. Our findings suggest that
    listeners are flexible in the cues they use for focus processing.
  • Cutler, A., Burchfield, L. A., & Antoniou, M. (2018). Factors affecting talker adaptation in a second language. In J. Epps, J. Wolfe, J. Smith, & C. Jones (Eds.), Proceedings of the 17th Australasian International Conference on Speech Science and Technology (pp. 33-36).

    Abstract

    Listeners adapt rapidly to previously unheard talkers by
    adjusting phoneme categories using lexical knowledge, in a
    process termed lexically-guided perceptual learning. Although
    this is firmly established for listening in the native language
    (L1), perceptual flexibility in second languages (L2) is as yet
    less well understood. We report two experiments examining L1
    and L2 perceptual learning, the first in Mandarin-English late
    bilinguals, the second in Australian learners of Mandarin. Both
    studies showed stronger learning in L1; in L2, however,
    learning appeared for the English-L1 group but not for the
    Mandarin-L1 group. Phonological mapping differences from
    the L1 to the L2 are suggested as the reason for this result.
  • Cutler, A., & Butterfield, S. (1989). Natural speech cues to word segmentation under difficult listening conditions. In J. Tubach, & J. Mariani (Eds.), Proceedings of Eurospeech 89: European Conference on Speech Communication and Technology: Vol. 2 (pp. 372-375). Edinburgh: CEP Consultants.

    Abstract

    One of a listener's major tasks in understanding continuous speech is segmenting the speech signal into separate words. When listening conditions are difficult, speakers can help listeners by deliberately speaking more clearly. In three experiments, we examined how word boundaries are produced in deliberately clear speech. We found that speakers do indeed attempt to mark word boundaries; moreover, they differentiate between word boundaries in a way which suggests they are sensitive to listener needs. Application of heuristic segmentation strategies makes word boundaries before strong syllables easiest for listeners to perceive; but under difficult listening conditions speakers pay more attention to marking word boundaries before weak syllables, i.e. they mark those boundaries which are otherwise particularly hard to perceive.
  • Cutler, A. (1990). Syllabic lengthening as a word boundary cue. In R. Seidl (Ed.), Proceedings of the 3rd Australian International Conference on Speech Science and Technology (pp. 324-328). Canberra: Australian Speech Science and Technology Association.

    Abstract

    Bisyllabic sequences which could be interpreted as one word or two were produced in sentence contexts by a trained speaker, and syllabic durations measured. Listeners judged whether the bisyllables, excised from context, were one word or two. The proportion of two-word choices correlated positively with measured duration, but only for bisyllables stressed on the second syllable. The results may suggest a limit for listener sensitivity to syllabic lengthening as a word boundary cue.
  • Cutler, A., Norris, D., & Van Ooijen, B. (1990). Vowels as phoneme detection targets. In Proceedings of the First International Conference on Spoken Language Processing (pp. 581-584).

    Abstract

    Phoneme detection is a psycholinguistic task in which listeners' response time to detect the presence of a pre-specified phoneme target is measured. Typically, detection tasks have used consonant targets. This paper reports two experiments in which subjects responded to vowels as phoneme detection targets. In the first experiment, targets occurred in real words, in the second in nonsense words. Response times were long by comparison with consonantal targets. Targets in initial syllables were responded to much more slowly than targets in second syllables. Strong vowels were responded to faster than reduced vowels in real words but not in nonwords. These results suggest that the process of phoneme detection produces different results for vowels and for consonants. We discuss possible explanations for this difference, in particular the possibility of language-specificity.
  • Delgado, T., Ravignani, A., Verhoef, T., Thompson, B., Grossi, T., & Kirby, S. (2018). Cultural transmission of melodic and rhythmic universals: Four experiments and a model. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 89-91). Toruń, Poland: NCU Press. doi:10.12775/3991-1.019.
  • Doumas, L. A. A., Martin, A. E., & Hummel, J. E. (2020). Relation learning in a neurocomputational architecture supports cross-domain transfer. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 932-937). Montreal, QB: Cognitive Science Society.

    Abstract

    Humans readily generalize, applying prior knowledge to novel situations and stimuli. Advances in machine learning have begun to approximate and even surpass human performance, but these systems struggle to generalize what they have learned to untrained situations. We present a model based on wellestablished neurocomputational principles that demonstrates human-level generalisation. This model is trained to play one video game (Breakout) and performs one-shot generalisation to a new game (Pong) with different characteristics. The model
    generalizes because it learns structured representations that are functionally symbolic (viz., a role-filler binding calculus) from unstructured training data. It does so without feedback, and without requiring that structured representations are specified a priori. Specifically, the model uses neural co-activation to discover which characteristics of the input are invariant and to learn relational predicates, and oscillatory regularities in network firing to bind predicates to arguments. To our knowledge,
    this is the first demonstration of human-like generalisation in a machine system that does not assume structured representa-
    tions to begin with.
  • Duarte, R., Uhlmann, M., Van den Broek, D., Fitz, H., Petersson, K. M., & Morrison, A. (2018). Encoding symbolic sequences with spiking neural reservoirs. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/IJCNN.2018.8489114.

    Abstract

    Biologically inspired spiking networks are an important tool to study the nature of computation and cognition in neural systems. In this work, we investigate the representational capacity of spiking networks engaged in an identity mapping task. We compare two schemes for encoding symbolic input, one in which input is injected as a direct current and one where input is delivered as a spatio-temporal spike pattern. We test the ability of networks to discriminate their input as a function of the number of distinct input symbols. We also compare performance using either membrane potentials or filtered spike trains as state variable. Furthermore, we investigate how the circuit behavior depends on the balance between excitation and inhibition, and the degree of synchrony and regularity in its internal dynamics. Finally, we compare different linear methods of decoding population activity onto desired target labels. Overall, our results suggest that even this simple mapping task is strongly influenced by design choices on input encoding, state-variables, circuit characteristics and decoding methods, and these factors can interact in complex ways. This work highlights the importance of constraining computational network models of behavior by available neurobiological evidence.
  • Ergin, R., Raviv, L., Senghas, A., Padden, C., & Sandler, W. (2020). Community structure affects convergence on uniform word orders: Evidence from emerging sign languages. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 84-86). Nijmegen: The Evolution of Language Conferences.
  • Ergin, R., Senghas, A., Jackendoff, R., & Gleitman, L. (2018). Structural cues for symmetry, asymmetry, and non-symmetry in Central Taurus Sign Language. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 104-106). Toruń, Poland: NCU Press. doi:10.12775/3991-1.025.
  • Galke, L., Gerstenkorn, G., & Scherp, A. (2018). A case study of closed-domain response suggestion with limited training data. In M. Elloumi, M. Granitzer, A. Hameurlain, C. Seifert, B. Stein, A. Min Tjoa, & R. Wagner (Eds.), Database and Expert Systems Applications: DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September 3–6, 2018, Proceedings (pp. 218-229). Cham, Switzerland: Springer.

    Abstract

    We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.
  • Galke, L., Mai, F., & Vagliano, I. (2018). Multi-modal adversarial autoencoders for recommendations of citations and subject labels. In T. Mitrovic, J. Zhang, L. Chen, & D. Chin (Eds.), UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 197-205). New York: ACM. doi:10.1145/3209219.3209236.

    Abstract

    We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model.
  • Harmon, Z., & Kapatsinski, V. (2020). The best-laid plan of mice and men: Competition between top-down and preceding-item cues in plan execution. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 1674-1680). Montreal, QB: Cognitive Science Society.

    Abstract

    There is evidence that the process of executing a planned utterance involves the use of both preceding-context and top-down cues. Utterance-initial words are cued only by the top-down plan. In contrast, non-initial words are cued both by top-down cues and preceding-context cues. Co-existence of both cue types raises the question of how they interact during learning. We argue that this interaction is competitive: items that tend to be preceded by predictive preceding-context cues are harder to activate from the plan without this predictive context. A novel computational model of this competition is developed. The model is tested on a corpus of repetition disfluencies and shown to account for the influences on patterns of restarts during production. In particular, this model predicts a novel Initiation Effect: following an interruption, speakers re-initiate production from words that tend to occur in utterance-initial position, even when they are not initial in the interrupted utterance.
  • Hashemzadeh, M., Kaufeld, G., White, M., Martin, A. E., & Fyshe, A. (2020). From language to language-ish: How brain-like is an LSTM representation of nonsensical language stimuli? In T. Cohn, Y. He, & Y. Liu (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 645-655). Association for Computational Linguistics.

    Abstract

    The representations generated by many mod-
    els of language (word embeddings, recurrent
    neural networks and transformers) correlate
    to brain activity recorded while people read.
    However, these decoding results are usually
    based on the brain’s reaction to syntactically
    and semantically sound language stimuli. In
    this study, we asked: how does an LSTM (long
    short term memory) language model, trained
    (by and large) on semantically and syntac-
    tically intact language, represent a language
    sample with degraded semantic or syntactic
    information? Does the LSTM representation
    still resemble the brain’s reaction? We found
    that, even for some kinds of nonsensical lan-
    guage, there is a statistically significant rela-
    tionship between the brain’s activity and the
    representations of an LSTM. This indicates
    that, at least in some instances, LSTMs and the
    human brain handle nonsensical data similarly.
  • De Heer Kloots, M., Carlson, D., Garcia, M., Kotz, S., Lowry, A., Poli-Nardi, L., de Reus, K., Rubio-García, A., Sroka, M., Varola, M., & Ravignani, A. (2020). Rhythmic perception, production and interactivity in harbour and grey seals. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 59-62). Nijmegen: The Evolution of Language Conferences.
  • Hoeksema, N., Villanueva, S., Mengede, J., Salazar-Casals, A., Rubio-García, A., Curcic-Blake, B., Vernes, S. C., & Ravignani, A. (2020). Neuroanatomy of the grey seal brain: Bringing pinnipeds into the neurobiological study of vocal learning. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 162-164). Nijmegen: The Evolution of Language Conferences.
  • Hoeksema, N., Wiesmann, M., Kiliaan, A., Hagoort, P., & Vernes, S. C. (2020). Bats and the comparative neurobiology of vocal learning. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 165-167). Nijmegen: The Evolution of Language Conferences.
  • Hopman, E., Thompson, B., Austerweil, J., & Lupyan, G. (2018). Predictors of L2 word learning accuracy: A big data investigation. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 513-518). Austin, TX: Cognitive Science Society.

    Abstract

    What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.
  • Huettig, F., Kolinsky, R., & Lachmann, T. (Eds.). (2018). The effects of literacy on cognition and brain functioning [Special Issue]. Language, Cognition and Neuroscience, 33(3).
  • Isbilen, E., Frost, R. L. A., Monaghan, P., & Christiansen, M. (2018). Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1856-1861). Austin, TX: Cognitive Science Society.

    Abstract

    A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.
  • Janssen, R., Moisik, S. R., & Dediu, D. (2018). Agent model reveals the influence of vocal tract anatomy on speech during ontogeny and glossogeny. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 171-174). Toruń, Poland: NCU Press. doi:10.12775/3991-1.042.
  • Kanero, J., Franko, I., Oranç, C., Uluşahin, O., Koskulu, S., Adigüzel, Z., Küntay, A. C., & Göksun, T. (2018). Who can benefit from robots? Effects of individual differences in robot-assisted language learning. In Proceedings of the 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 212-217). Piscataway, NJ, USA: IEEE.

    Abstract

    It has been suggested that some individuals may benefit more from social robots than do others. Using second
    language (L2) as an example, the present study examined how individual differences in attitudes toward robots and personality
    traits may be related to learning outcomes. Preliminary results with 24 Turkish-speaking adults suggest that negative attitudes
    toward robots, more specifically thoughts and anxiety about the negative social impact that robots may have on the society,
    predicted how well adults learned L2 words from a social robot. The possible implications of the findings as well as future directions are also discussed
  • Kempen, G., & Hoenkamp, E. (1982). Incremental sentence generation: Implications for the structure of a syntactic processor. In J. Horecký (Ed.), COLING 82. Proceedings of the Ninth International Conference on Computational Linguistics, Prague, July 5-10, 1982 (pp. 151-156). Amsterdam: North-Holland.

    Abstract

    Human speakers often produce sentences incrementally. They can start speaking having in mind only a fragmentary idea of what they want to say, and while saying this they refine the contents underlying subsequent parts of the utterance. This capability imposes a number of constraints on the design of a syntactic processor. This paper explores these constraints and evaluates some recent computational sentence generators from the perspective of incremental production.
  • Khoe, Y. H., Tsoukala, C., Kootstra, G. J., & Frank, S. L. (2020). Modeling cross-language structural priming in sentence production. In T. C. Stewart (Ed.), Proceedings of the 18th Annual Meeting of the International Conference on Cognitive Modeling (pp. 131-137). University Park, PA, USA: The Penn State Applied Cognitive Science Lab.

    Abstract

    A central question in the psycholinguistic study of multilingualism is how syntax is shared across languages. We implement a model to investigate whether error-based implicit learning can provide an account of cross-language structural priming. The model is based on the Dual-path model of
    sentence-production (Chang, 2002). We implement our model using the Bilingual version of Dual-path (Tsoukala, Frank, & Broersma, 2017). We answer two main questions: (1) Can structural priming of active and passive constructions occur between English and Spanish in a bilingual version of the Dual-
    path model? (2) Does cross-language priming differ quantitatively from within-language priming in this model? Our results show that cross-language priming does occur in the model. This finding adds to the viability of implicit learning as an account of structural priming in general and cross-language
    structural priming specifically. Furthermore, we find that the within-language priming effect is somewhat stronger than the cross-language effect. In the context of mixed results from
    behavioral studies, we interpret the latter finding as an indication that the difference between cross-language and within-
    language priming is small and difficult to detect statistically.
  • Klein, W. (Ed.). (1989). Kindersprache [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (73).
  • Klein, W. (Ed.). (1990). Sprache und Raum [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (78).
  • Klein, W., & Schlieben-Lange, B. (Eds.). (1990). Zukunft der Sprache [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (79).
  • Klein, W. (Ed.). (1982). Zweitspracherwerb [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (45).
  • Lattenkamp, E. Z., Linnenschmidt, M., Mardus, E., Vernes, S. C., Wiegrebe, L., & Schutte, M. (2020). Impact of auditory feedback on bat vocal development. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 249-251). Nijmegen: The Evolution of Language Conferences.
  • Lattenkamp, E. Z., Vernes, S. C., & Wiegrebe, L. (2018). Mammalian models for the study of vocal learning: A new paradigm in bats. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 235-237). Toruń, Poland: NCU Press. doi:10.12775/3991-1.056.
  • Lauscher, A., Eckert, K., Galke, L., Scherp, A., Rizvi, S. T. R., Ahmed, S., Dengel, A., Zumstein, P., & Klein, A. (2018). Linked open citation database: Enabling libraries to contribute to an open and interconnected citation graph. In J. Chen, M. A. Gonçalves, J. M. Allen, E. A. Fox, M.-Y. Kan, & V. Petras (Eds.), JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (pp. 109-118). New York: ACM. doi:10.1145/3197026.3197050.

    Abstract

    Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloging workflow in libraries nowadays.
    In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloging citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility.
  • Lefever, E., Hendrickx, I., Croijmans, I., Van den Bosch, A., & Majid, A. (2018). Discovering the language of wine reviews: A text mining account. In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 3297-3302). Paris: LREC.

    Abstract

    It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the wine's color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged.
  • Lei, L., Raviv, L., & Alday, P. M. (2020). Using spatial visualizations and real-world social networks to understand language evolution and change. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 252-254). Nijmegen: The Evolution of Language Conferences.
  • Levelt, W. J. M., & Plomp, R. (1962). Musical consonance and critical bandwidth. In Proceedings of the 4th International Congress Acoustics (pp. 55-55).
  • Levshina, N. (2020). How tight is your language? A semantic typology based on Mutual Information. In K. Evang, L. Kallmeyer, R. Ehren, S. Petitjean, E. Seyffarth, & D. Seddah (Eds.), Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories (pp. 70-78). Düsseldorf, Germany: Association for Computational Linguistics. doi:10.18653/v1/2020.tlt-1.7.

    Abstract

    Languages differ in the degree of semantic flexibility of their syntactic roles. For example, Eng-
    lish and Indonesian are considered more flexible with regard to the semantics of subjects,
    whereas German and Japanese are less flexible. In Hawkins’ classification, more flexible lan-
    guages are said to have a loose fit, and less flexible ones are those that have a tight fit. This
    classification has been based on manual inspection of example sentences. The present paper
    proposes a new, quantitative approach to deriving the measures of looseness and tightness from
    corpora. We use corpora of online news from the Leipzig Corpora Collection in thirty typolog-
    ically and genealogically diverse languages and parse them syntactically with the help of the
    Universal Dependencies annotation software. Next, we compute Mutual Information scores for
    each language using the matrices of lexical lemmas and four syntactic dependencies (intransi-
    tive subjects, transitive subject, objects and obliques). The new approach allows us not only to
    reproduce the results of previous investigations, but also to extend the typology to new lan-
    guages. We also demonstrate that verb-final languages tend to have a tighter relationship be-
    tween lexemes and syntactic roles, which helps language users to recognize thematic roles early
    during comprehension.

    Additional information

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  • Lopopolo, A., Frank, S. L., Van den Bosch, A., Nijhof, A., & Willems, R. M. (2018). The Narrative Brain Dataset (NBD), an fMRI dataset for the study of natural language processing in the brain. In B. Devereux, E. Shutova, & C.-R. Huang (Eds.), Proceedings of LREC 2018 Workshop "Linguistic and Neuro-Cognitive Resources (LiNCR) (pp. 8-11). Paris: LREC.

    Abstract

    We present the Narrative Brain Dataset, an fMRI dataset that was collected during spoken presentation of short excerpts of three
    stories in Dutch. Together with the brain imaging data, the dataset contains the written versions of the stimulation texts. The texts are
    accompanied with stochastic (perplexity and entropy) and semantic computational linguistic measures. The richness and unconstrained
    nature of the data allows the study of language processing in the brain in a more naturalistic setting than is common for fMRI studies.
    We hope that by making NBD available we serve the double purpose of providing useful neural data to researchers interested in natural
    language processing in the brain and to further stimulate data sharing in the field of neuroscience of language.
  • Lupyan, G., Wendorf, A., Berscia, L. M., & Paul, J. (2018). Core knowledge or language-augmented cognition? The case of geometric reasoning. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 252-254). Toruń, Poland: NCU Press. doi:10.12775/3991-1.062.
  • MacDonald, K., Räsänen, O., Casillas, M., & Warlaumont, A. S. (2020). Measuring prosodic predictability in children’s home language environments. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 695-701). Montreal, QB: Cognitive Science Society.

    Abstract

    Children learn language from the speech in their home environment. Recent work shows that more infant-directed speech
    (IDS) leads to stronger lexical development. But what makes IDS a particularly useful learning signal? Here, we expand on an attention-based account first proposed by Räsänen et al. (2018): that prosodic modifications make IDS less predictable, and thus more interesting. First, we reproduce the critical finding from Räsänen et al.: that lab-recorded IDS pitch is less predictable compared to adult-directed speech (ADS). Next, we show that this result generalizes to the home language environment, finding that IDS in daylong recordings is also less predictable than ADS but that this pattern is much less robust than for IDS recorded in the lab. These results link experimental work on attention and prosodic modifications of IDS to real-world language-learning environments, highlighting some challenges of scaling up analyses of IDS to larger datasets that better capture children’s actual input.
  • Mai, F., Galke, L., & Scherp, A. (2018). Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text. In J. Chen, M. A. Gonçalves, J. M. Allen, E. A. Fox, M.-Y. Kan, & V. Petras (Eds.), JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries (pp. 169-178). New York: ACM.

    Abstract

    For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.4%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.
  • Yu, J., Mailhammer, R., & Cutler, A. (2020). Vocabulary structure affects word recognition: Evidence from German listeners. In N. Minematsu, M. Kondo, T. Arai, & R. Hayashi (Eds.), Proceedings of Speech Prosody 2020 (pp. 474-478). Tokyo: ISCA. doi:10.21437/SpeechProsody.2020-97.

    Abstract

    Lexical stress is realised similarly in English, German, and
    Dutch. On a suprasegmental level, stressed syllables tend to be
    longer and more acoustically salient than unstressed syllables;
    segmentally, vowels in unstressed syllables are often reduced.
    The frequency of unreduced unstressed syllables (where only
    the suprasegmental cues indicate lack of stress) however,
    differs across the languages. The present studies test whether
    listener behaviour is affected by these vocabulary differences,
    by investigating German listeners’ use of suprasegmental cues
    to lexical stress in German and English word recognition. In a
    forced-choice identification task, German listeners correctly
    assigned single-syllable fragments (e.g., Kon-) to one of two
    words differing in stress (KONto, konZEPT). Thus, German
    listeners can exploit suprasegmental information for
    identifying words. German listeners also performed above
    chance in a similar task in English (with, e.g., DIver, diVERT),
    i.e., their sensitivity to these cues also transferred to a nonnative
    language. An English listener group, in contrast, failed
    in the English fragment task. These findings mirror vocabulary
    patterns: German has more words with unreduced unstressed
    syllables than English does.
  • Mengede, J., Devanna, P., Hörpel, S. G., Firzla, U., & Vernes, S. C. (2020). Studying the genetic bases of vocal learning in bats. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 280-282). Nijmegen: The Evolution of Language Conferences.
  • Merkx, D., & Scharenborg, O. (2018). Articulatory feature classification using convolutional neural networks. In Proceedings of Interspeech 2018 (pp. 2142-2146). doi:10.21437/Interspeech.2018-2275.

    Abstract

    The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are investigating articulatory feature classifiers that are able to create reliable and accurate transcriptions of the articulatory behaviour encoded in the acoustic speech signal. Articulatory feature (AF) modelling of speech has received a considerable amount of attention in automatic speech recognition research. Different approaches have been used to build AF classifiers, most notably multi-layer perceptrons. Recently, deep neural networks have been applied to the task of AF classification. This paper aims to improve AF classification by investigating two different approaches: 1) investigating the usefulness of a deep Convolutional neural network (CNN) for AF classification; 2) integrating the Mel filtering operation into the CNN architecture. The results showed a remarkable improvement in classification accuracy of the CNNs over state-of-the-art AF classification results for Dutch, most notably in the minority classes. Integrating the Mel filtering operation into the CNN architecture did not further improve classification performance.
  • Micklos, A., Macuch Silva, V., & Fay, N. (2018). The prevalence of repair in studies of language evolution. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 316-318). Toruń, Poland: NCU Press. doi:10.12775/3991-1.075.
  • Mudd, K., Lutzenberger, H., De Vos, C., Fikkert, P., Crasborn, O., & De Boer, B. (2020). How does social structure shape language variation? A case study of the Kata Kolok lexicon. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 302-304). Nijmegen: The Evolution of Language Conferences.
  • Mulder, K., Ten Bosch, L., & Boves, L. (2018). Analyzing EEG Signals in Auditory Speech Comprehension Using Temporal Response Functions and Generalized Additive Models. In Proceedings of Interspeech 2018 (pp. 1452-1456). doi:10.21437/Interspeech.2018-1676.

    Abstract

    Analyzing EEG signals recorded while participants are listening to continuous speech with the purpose of testing linguistic hypotheses is complicated by the fact that the signals simultaneously reflect exogenous acoustic excitation and endogenous linguistic processing. This makes it difficult to trace subtle differences that occur in mid-sentence position. We apply an analysis based on multivariate temporal response functions to uncover subtle mid-sentence effects. This approach is based on a per-stimulus estimate of the response of the neural system to speech input. Analyzing EEG signals predicted on the basis of the response functions might then bring to light conditionspecific differences in the filtered signals. We validate this approach by means of an analysis of EEG signals recorded with isolated word stimuli. Then, we apply the validated method to the analysis of the responses to the same words in the middle of meaningful sentences.
  • Ozyurek, A. (2020). From hands to brains: How does human body talk, think and interact in face-to-face language use? In K. Truong, D. Heylen, & M. Czerwinski (Eds.), ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction (pp. 1-2). New York, NY, USA: Association for Computing Machinery. doi:10.1145/3382507.3419442.
  • Paplu, S. H., Mishra, C., & Berns, K. (2020). Pseudo-randomization in automating robot behaviour during human-robot interaction. In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 1-6). Institute of Electrical and Electronics Engineers. doi:10.1109/ICDL-EpiRob48136.2020.9278115.

    Abstract

    Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach.

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  • Räsänen, O., Seshadri, S., & Casillas, M. (2018). Comparison of syllabification algorithms and training strategies for robust word count estimation across different languages and recording conditions. In Proceedings of Interspeech 2018 (pp. 1200-1204). doi:10.21437/Interspeech.2018-1047.

    Abstract

    Word count estimation (WCE) from audio recordings has a number of applications, including quantifying the amount of speech that language-learning infants hear in their natural environments, as captured by daylong recordings made with devices worn by infants. To be applicable in a wide range of scenarios and also low-resource domains, WCE tools should be extremely robust against varying signal conditions and require minimal access to labeled training data in the target domain. For this purpose, earlier work has used automatic syllabification of speech, followed by a least-squares-mapping of syllables to word counts. This paper compares a number of previously proposed syllabifiers in the WCE task, including a supervised bi-directional long short-term memory (BLSTM) network that is trained on a language for which high quality syllable annotations are available (a “high resource language”), and reports how the alternative methods compare on different languages and signal conditions. We also explore additive noise and varying-channel data augmentation strategies for BLSTM training, and show how they improve performance in both matching and mismatching languages. Intriguingly, we also find that even though the BLSTM works on languages beyond its training data, the unsupervised algorithms can still outperform it in challenging signal conditions on novel languages.
  • Rasenberg, M., Dingemanse, M., & Ozyurek, A. (2020). Lexical and gestural alignment in interaction and the emergence of novel shared symbols. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 356-358). Nijmegen: The Evolution of Language Conferences.
  • Ravignani, A., Garcia, M., Gross, S., de Reus, K., Hoeksema, N., Rubio-Garcia, A., & de Boer, B. (2018). Pinnipeds have something to say about speech and rhythm. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 399-401). Toruń, Poland: NCU Press. doi:10.12775/3991-1.095.
  • Raviv, L., Meyer, A. S., & Lev-Ari, S. (2018). The role of community size in the emergence of linguistic structure. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 402-404). Toruń, Poland: NCU Press. doi:10.12775/3991-1.096.
  • Raviv, L., Meyer, A. S., & Lev-Ari, S. (2020). Network structure and the cultural evolution of linguistic structure: A group communication experiment. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 359-361). Nijmegen: The Evolution of Language Conferences.
  • de Reus, K., Carlson, D., Jadoul, Y., Lowry, A., Gross, S., Garcia, M., Salazar-Casals, A., Rubio-García, A., Haas, C. E., De Boer, B., & Ravignani, A. (2020). Relationships between vocal ontogeny and vocal tract anatomy in harbour seals (Phoca vitulina). In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 63-66). Nijmegen: The Evolution of Language Conferences.
  • Rubio-Fernández, P., & Jara-Ettinger, J. (2018). Joint inferences of speakers’ beliefs and referents based on how they speak. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 991-996). Austin, TX: Cognitive Science Society.

    Abstract

    For almost two decades, the poor performance observed with the so-called Director task has been interpreted as evidence of limited use of Theory of Mind in communication. Here we propose a probabilistic model of common ground in referential communication that derives three inferences from an utterance: what the speaker is talking about in a visual context, what she knows about the context, and what referential expressions she prefers. We tested our model by comparing its inferences with those made by human participants and found that it closely mirrors their judgments, whereas an alternative model compromising the hearer’s expectations of cooperativeness and efficiency reveals a worse fit to the human data. Rather than assuming that common ground is fixed in a given exchange and may or may not constrain reference resolution, we show how common ground can be inferred as part of the process of reference assignment.
  • Saleh, A., Beck, T., Galke, L., & Scherp, A. (2018). Performance comparison of ad-hoc retrieval models over full-text vs. titles of documents. In M. Dobreva, A. Hinze, & M. Žumer (Eds.), Maturity and Innovation in Digital Libraries: 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Hamilton, New Zealand, November 19-22, 2018, Proceedings (pp. 290-303). Cham, Switzerland: Springer.

    Abstract

    While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e.g., copyright policies of academic publishers. On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. We use different datasets, including the three digital library datasets: EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only 3% less than those of the best full-text-based retrieval models.
  • Scharenborg, O., & Merkx, D. (2018). The role of articulatory feature representation quality in a computational model of human spoken-word recognition. In Proceedings of the Machine Learning in Speech and Language Processing Workshop (MLSLP 2018).

    Abstract

    Fine-Tracker is a speech-based model of human speech
    recognition. While previous work has shown that Fine-Tracker
    is successful at modelling aspects of human spoken-word
    recognition, its speech recognition performance is not
    comparable to that of human performance, possibly due to
    suboptimal intermediate articulatory feature (AF)
    representations. This study investigates the effect of improved
    AF representations, obtained using a state-of-the-art deep
    convolutional network, on Fine-Tracker’s simulation and
    recognition performance: Although the improved AF quality
    resulted in improved speech recognition; it, surprisingly, did
    not lead to an improvement in Fine-Tracker’s simulation power.
  • Scott, D. R., & Cutler, A. (1982). Segmental cues to syntactic structure. In Proceedings of the Institute of Acoustics 'Spectral Analysis and its Use in Underwater Acoustics' (pp. E3.1-E3.4). London: Institute of Acoustics.
  • Seidlmayer, E., Voß, J., Melnychuk, T., Galke, L., Tochtermann, K., Schultz, C., & Förstner, K. U. (2020). ORCID for Wikidata. Data enrichment for scientometric applications. In L.-A. Kaffee, O. Tifrea-Marciuska, E. Simperl, & D. Vrandečić (Eds.), Proceedings of the 1st Wikidata Workshop (Wikidata 2020). Aachen, Germany: CEUR Workshop Proceedings.

    Abstract

    Due to its numerous bibliometric entries of scholarly articles and connected information Wikidata can serve as an open and rich
    source for deep scientometrical analyses. However, there are currently certain limitations: While 31.5% of all Wikidata entries represent scientific articles, only 8.9% are entries describing a person and the number
    of entries researcher is accordingly even lower. Another issue is the frequent absence of established relations between the scholarly article item and the author item although the author is already listed in Wikidata.
    To fill this gap and to improve the content of Wikidata in general, we established a workflow for matching authors and scholarly publications by integrating data from the ORCID (Open Researcher and Contributor ID) database. By this approach we were able to extend Wikidata by more than 12k author-publication relations and the method can be
    transferred to other enrichments based on ORCID data. This is extension is beneficial for Wikidata users performing bibliometrical analyses or using such metadata for other purposes.
  • Seuren, P. A. M., & Mufwene, S. S. (Eds.). (1990). Issues in Creole lingusitics [Special Issue]. Linguistics, 28(4).
  • Seuren, P. A. M. (1982). Riorientamenti metodologici nello studio della variabilità linguistica. In D. Gambarara, & A. D'Atri (Eds.), Ideologia, filosofia e linguistica: Atti del Convegno Internazionale di Studi, Rende (CS) 15-17 Settembre 1978 ( (pp. 499-515). Roma: Bulzoni.
  • De Smedt, K., & Kempen, G. (1990). Discontinuous constituency in Segment Grammar. In Proceedings of the Symposium on Discontinuous Constituency. Tilburg: University of Brabant.
  • Speed, L., & Majid, A. (2018). Music and odor in harmony: A case of music-odor synaesthesia. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 2527-2532). Austin, TX: Cognitive Science Society.

    Abstract

    We report an individual with music-odor synaesthesia who experiences automatic and vivid odor sensations when she hears music. S’s odor associations were recorded on two days, and compared with those of two control participants. Overall, S produced longer descriptions, and her associations were of multiple odors at once, in comparison to controls who typically reported a single odor. Although odor associations were qualitatively different between S and controls, ratings of the consistency of their descriptions did not differ. This demonstrates that crossmodal associations between music and odor exist in non-synaesthetes too. We also found that S is better at discriminating between odors than control participants, and is more likely to experience emotion, memories and evaluations triggered by odors, demonstrating the broader impact of her synaesthesia.

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  • Ten Bosch, L., Ernestus, M., & Boves, L. (2018). Analyzing reaction time sequences from human participants in auditory experiments. In Proceedings of Interspeech 2018 (pp. 971-975). doi:10.21437/Interspeech.2018-1728.

    Abstract

    Sequences of reaction times (RT) produced by participants in an experiment are not only influenced by the stimuli, but by many other factors as well, including fatigue, attention, experience, IQ, handedness, etc. These confounding factors result in longterm effects (such as a participant’s overall reaction capability) and in short- and medium-time fluctuations in RTs (often referred to as ‘local speed effects’). Because stimuli are usually presented in a random sequence different for each participant, local speed effects affect the underlying ‘true’ RTs of specific trials in different ways across participants. To be able to focus statistical analysis on the effects of the cognitive process under study, it is necessary to reduce the effect of confounding factors as much as possible. In this paper we propose and compare techniques and criteria for doing so, with focus on reducing (‘filtering’) the local speed effects. We show that filtering matters substantially for the significance analyses of predictors in linear mixed effect regression models. The performance of filtering is assessed by the average between-participant correlation between filtered RT sequences and by Akaike’s Information Criterion, an important measure of the goodness-of-fit of linear mixed effect regression models.
  • Ten Bosch, L., & Boves, L. (2018). Information encoding by deep neural networks: what can we learn? In Proceedings of Interspeech 2018 (pp. 1457-1461). doi:10.21437/Interspeech.2018-1896.

    Abstract

    The recent advent of deep learning techniques in speech tech-nology and in particular in automatic speech recognition hasyielded substantial performance improvements. This suggeststhat deep neural networks (DNNs) are able to capture structurein speech data that older methods for acoustic modeling, suchas Gaussian Mixture Models and shallow neural networks failto uncover. In image recognition it is possible to link repre-sentations on the first couple of layers in DNNs to structuralproperties of images, and to representations on early layers inthe visual cortex. This raises the question whether it is possi-ble to accomplish a similar feat with representations on DNNlayers when processing speech input. In this paper we presentthree different experiments in which we attempt to untanglehow DNNs encode speech signals, and to relate these repre-sentations to phonetic knowledge, with the aim to advance con-ventional phonetic concepts and to choose the topology of aDNNs more efficiently. Two experiments investigate represen-tations formed by auto-encoders. A third experiment investi-gates representations on convolutional layers that treat speechspectrograms as if they were images. The results lay the basisfor future experiments with recursive networks.
  • Ter Bekke, M., Drijvers, L., & Holler, J. (2020). The predictive potential of hand gestures during conversation: An investigation of the timing of gestures in relation to speech. In Proceedings of the 7th GESPIN - Gesture and Speech in Interaction Conference. Stockholm: KTH Royal Institute of Technology.

    Abstract

    In face-to-face conversation, recipients might use the bodily movements of the speaker (e.g. gestures) to facilitate language processing. It has been suggested that one way through which this facilitation may happen is prediction. However, for this to be possible, gestures would need to precede speech, and it is unclear whether this is true during natural conversation.
    In a corpus of Dutch conversations, we annotated hand gestures that represent semantic information and occurred during questions, and the word(s) which corresponded most closely to the gesturally depicted meaning. Thus, we tested whether representational gestures temporally precede their lexical affiliates. Further, to see whether preceding gestures may indeed facilitate language processing, we asked whether the gesture-speech asynchrony predicts the response time to the question the gesture is part of.
    Gestures and their strokes (most meaningful movement component) indeed preceded the corresponding lexical information, thus demonstrating their predictive potential. However, while questions with gestures got faster responses than questions without, there was no evidence that questions with larger gesture-speech asynchronies get faster responses. These results suggest that gestures indeed have the potential to facilitate predictive language processing, but further analyses on larger datasets are needed to test for links between asynchrony and processing advantages.
  • Thompson, B., Raviv, L., & Kirby, S. (2020). Complexity can be maintained in small populations: A model of lexical variability in emerging sign languages. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 440-442). Nijmegen: The Evolution of Language Conferences.
  • Thompson, B., & Lupyan, G. (2018). Automatic estimation of lexical concreteness in 77 languages. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1122-1127). Austin, TX: Cognitive Science Society.

    Abstract

    We estimate lexical Concreteness for millions of words across 77 languages. Using a simple regression framework, we combine vector-based models of lexical semantics with experimental norms of Concreteness in English and Dutch. By applying techniques to align vector-based semantics across distinct languages, we compute and release Concreteness estimates at scale in numerous languages for which experimental norms are not currently available. This paper lays out the technique and its efficacy. Although this is a difficult dataset to evaluate immediately, Concreteness estimates computed from English correlate with Dutch experimental norms at $\rho$ = .75 in the vocabulary at large, increasing to $\rho$ = .8 among Nouns. Our predictions also recapitulate attested relationships with word frequency. The approach we describe can be readily applied to numerous lexical measures beyond Concreteness
  • Thompson, B., Roberts, S., & Lupyan, G. (2018). Quantifying semantic similarity across languages. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 2551-2556). Austin, TX: Cognitive Science Society.

    Abstract

    Do all languages convey semantic knowledge in the same way? If language simply mirrors the structure of the world, the answer should be a qualified “yes”. If, however, languages impose structure as much as reflecting it, then even ostensibly the “same” word in different languages may mean quite different things. We provide a first pass at a large-scale quantification of cross-linguistic semantic alignment of approximately 1000 meanings in 55 languages. We find that the translation equivalents in some domains (e.g., Time, Quantity, and Kinship) exhibit high alignment across languages while the structure of other domains (e.g., Politics, Food, Emotions, and Animals) exhibits substantial cross-linguistic variability. Our measure of semantic alignment correlates with known phylogenetic distances between languages: more phylogenetically distant languages have less semantic alignment. We also find semantic alignment to correlate with cultural distances between societies speaking the languages, suggesting a rich co-adaptation of language and culture even in domains of experience that appear most constrained by the natural world
  • Tourtouri, E. N., Delogu, F., & Crocker, M. W. (2018). Specificity and entropy reduction in situated referential processing. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 3356-3361). Austin: Cognitive Science Society.

    Abstract

    In situated communication, reference to an entity in the shared visual context can be established using eitheranexpression that conveys precise (minimally specified) or redundant (over-specified) information. There is, however, along-lasting debate in psycholinguistics concerningwhether the latter hinders referential processing. We present evidence from an eyetrackingexperiment recordingfixations as well asthe Index of Cognitive Activity –a novel measure of cognitive workload –supporting the view that over-specifications facilitate processing. We further present originalevidence that, above and beyond the effect of specificity,referring expressions thatuniformly reduce referential entropyalso benefitprocessing
  • Tsoukala, C., Frank, S. L., Van den Bosch, A., Kroff, J. V., & Broersma, M. (2020). Simulating Spanish-English code-switching: El modelo está generating code-switches. In E. Chersoni, C. Jacobs, Y. Oseki, L. Prévot, & E. Santus (Eds.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 20-29). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL).

    Abstract

    Multilingual speakers are able to switch from
    one language to the other (“code-switch”) be-
    tween or within sentences. Because the under-
    lying cognitive mechanisms are not well un-
    derstood, in this study we use computational
    cognitive modeling to shed light on the pro-
    cess of code-switching. We employed the
    Bilingual Dual-path model, a Recurrent Neu-
    ral Network of bilingual sentence production
    (Tsoukala et al., 2017) and simulated sentence
    production in simultaneous Spanish-English
    bilinguals. Our first goal was to investigate
    whether the model would code-switch with-
    out being exposed to code-switched training
    input. The model indeed produced code-
    switches even without any exposure to such
    input and the patterns of code-switches are
    in line with earlier linguistic work (Poplack,
    1980). The second goal of this study was to
    investigate an auxiliary phrase asymmetry that
    exists in Spanish-English code-switched pro-
    duction. Using this cognitive model, we ex-
    amined a possible cause for this asymmetry.
    To our knowledge, this is the first computa-
    tional cognitive model that aims to simulate
    code-switched sentence production.
  • Vagliano, I., Galke, L., Mai, F., & Scherp, A. (2018). Using adversarial autoencoders for multi-modal automatic playlist continuation. In C.-W. Chen, P. Lamere, M. Schedl, & H. Zamani (Eds.), RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 5.1-5.6). New York: ACM. doi:10.1145/3267471.3267476.

    Abstract

    The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.
  • Van den Heuvel, H., Oostdijk, N., Rowland, C. F., & Trilsbeek, P. (2020). The CLARIN Knowledge Centre for Atypical Communication Expertise. In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the 12th Language Resources and Evaluation Conference (LREC 2020) (pp. 3312-3316). Marseille, France: European Language Resources Association.

    Abstract

    This paper introduces a new CLARIN Knowledge Center which is the K-Centre for Atypical Communication Expertise (ACE for short) which has been established at the Centre for Language and Speech Technology (CLST) at Radboud University. Atypical communication is an umbrella term used here to denote language use by second language learners, people with language disorders or those suffering from language disabilities, but also more broadly by bilinguals and users of sign languages. It involves multiple modalities (text, speech, sign, gesture) and encompasses different developmental stages. ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. We explain the mission of ACE and show its potential on a number of showcases and a use case.
  • Van Dooren, A. (2020). The temporal perspective of epistemics in Dutch. In M. Franke, N. Kompa, M. Liu, J. L. Mueller, & J. Schwab (Eds.), Proceedings of Sinn Und Bedeutung 24 (pp. 143-160). Osnabrück: Osnabrück University.

    Abstract

    A series of experiments is conducted on naïve native speakers of Dutch and English to study the scope relation between tense and epistemic modality. The results are consistent with the claim that epistemics scope over tense (Stowell 2004, Hacquard 2006, a.o.), and challenge recent research that states that epistemics can, or must, scope under tense (von Fintel and Gillies 2007, Rullmann & Matthewson 2018): Dutch and English participants in a Truth Value Judgment Task judge sentences to be false when the past tense forms of the modals have to and moeten 'have to' are used to make an epistemic claim that held at a time before speech time, and true when they are used to make an epistemic claim that holds at speech time. Moreover, English participants in an Acceptability Judgment Task judge sentences to be infelicitous when the same past tense form of have to is used to make an epistemic claim that held at a time before speech time. Besides these general patterns, the results show variation within and across the two languages, which leads to interesting new questions about the interaction between tense and (epistemic) modality.
  • Van Arkel, J., Woensdregt, M., Dingemanse, M., & Blokpoel, M. (2020). A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis. In R. Fernández, & T. Linzen (Eds.), Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020) (pp. 177-194). Stroudsburg, PA, USA: The Association for Computational Linguistics. doi:10.18653/v1/2020.conll-1.14.

    Abstract

    How can people communicate successfully while keeping resource costs low in the face of ambiguity? We present a principled theoretical analysis comparing two strategies for disambiguation in communication: (i) pragmatic reasoning, where communicators reason about each other, and (ii) other-initiated repair, where communicators signal and resolve trouble interactively. Using agent-based simulations and computational complexity analyses, we compare the efficiency of these strategies in terms of communicative success, computation cost and interaction cost. We show that agents with a simple repair mechanism can increase efficiency, compared to pragmatic agents, by reducing their computational burden at the cost of longer interactions. We also find that efficiency is highly contingent on the mechanism, highlighting the importance of explicit formalisation and computational rigour.
  • Vernes, S. C. (2020). Understanding bat vocal learning to gain insight into speech and language. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 6). Nijmegen: The Evolution of Language Conferences.
  • Vernes, S. C. (2018). Vocal learning in bats: From genes to behaviour. In C. Cuskley, M. Flaherty, H. Little, L. McCrohon, A. Ravignani, & T. Verhoef (Eds.), Proceedings of the 12th International Conference on the Evolution of Language (EVOLANG XII) (pp. 516-518). Toruń, Poland: NCU Press. doi:10.12775/3991-1.128.
  • Von Holzen, K., & Bergmann, C. (2018). A Meta-Analysis of Infants’ Mispronunciation Sensitivity Development. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1159-1164). Austin, TX: Cognitive Science Society.

    Abstract

    Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.
  • Woensdregt, M., & Dingemanse, M. (2020). Other-initiated repair can facilitate the emergence of compositional language. In A. Ravignani, C. Barbieri, M. Flaherty, Y. Jadoul, E. Lattenkamp, H. Little, M. Martins, K. Mudd, & T. Verhoef (Eds.), The Evolution of Language: Proceedings of the 13th International Conference (Evolang13) (pp. 474-476). Nijmegen: The Evolution of Language Conferences.
  • Yang, J., Van den Bosch, A., & Frank, S. L. (2020). Less is Better: A cognitively inspired unsupervised model for language segmentation. In M. Zock, E. Chersoni, A. Lenci, & E. Santus (Eds.), Proceedings of the Workshop on the Cognitive Aspects of the Lexicon ( 28th International Conference on Computational Linguistics) (pp. 33-45). Stroudsburg: Association for Computational Linguistics.

    Abstract

    Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.

    Additional information

    full text via ACL website
  • Zhang, Y., Amatuni, A., Crain, E., & Yu, C. (2020). Seeking meaning: Examining a cross-situational solution to learn action verbs using human simulation paradigm. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020) (pp. 2854-2860). Montreal, QB: Cognitive Science Society.

    Abstract

    To acquire the meaning of a verb, language learners not only need to find the correct mapping between a specific verb and an action or event in the world, but also infer the underlying relational meaning that the verb encodes. Most verb naming instances in naturalistic contexts are highly ambiguous as many possible actions can be embedded in the same scenario and many possible verbs can be used to describe those actions. To understand whether learners can find the correct verb meaning from referentially ambiguous learning situations, we conducted three experiments using the Human Simulation Paradigm with adult learners. Our results suggest that although finding the right verb meaning from one learning instance is hard, there is a statistical solution to this problem. When provided with multiple verb learning instances all referring to the same verb, learners are able to aggregate information across situations and gradually converge to the correct semantic space. Even in cases where they may not guess the exact target verb, they can still discover the right meaning by guessing a similar verb that is semantically close to the ground truth.

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