Publications

Displaying 1 - 28 of 28
  • 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).

    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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 645-655).

    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., 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.
  • 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.
  • 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.
  • 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

    full text via ACL website
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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

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