Publications

Displaying 1 - 54 of 54
  • 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.
  • 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.
  • Cutler, A., & Fear, B. D. (1991). Categoricality in acceptability judgements for strong versus weak vowels. In J. Llisterri (Ed.), Proceedings of the ESCA Workshop on Phonetics and Phonology of Speaking Styles (pp. 18.1-18.5). Barcelona, Catalonia: Universitat Autonoma de Barcelona.

    Abstract

    A distinction between strong and weak vowels can be drawn on the basis of vowel quality, of stress, or of both factors. An experiment was conducted in which sets of contextually matched word-intial vowels ranging from clearly strong to clearly weak were cross-spliced, and the naturalness of the resulting words was rated by listeners. The ratings showed that in general cross-spliced words were only significantly less acceptable than unspliced words when schwa was not involved; this supports a categorical distinction based on vowel quality.
  • Cutler, A. (1987). Components of prosodic effects in speech recognition. In Proceedings of the Eleventh International Congress of Phonetic Sciences: Vol. 1 (pp. 84-87). Tallinn: Academy of Sciences of the Estonian SSR, Institute of Language and Literature.

    Abstract

    Previous research has shown that listeners use the prosodic structure of utterances in a predictive fashion in sentence comprehension, to direct attention to accented words. Acoustically identical words spliced into sentence contexts arc responded to differently if the prosodic structure of the context is \ aricd: when the preceding prosody indicates that the word will he accented, responses are faster than when the preceding prosodv is inconsistent with accent occurring on that word. In the present series of experiments speech hybridisation techniques were first used to interchange the timing patterns within pairs of prosodic variants of utterances, independently of the pitch and intensity contours. The time-adjusted utterances could then serve as a basis lor the orthogonal manipulation of the three prosodic dimensions of pilch, intensity and rhythm. The overall pattern of results showed that when listeners use prosody to predict accent location, they do not simply rely on a single prosodic dimension, hut exploit the interaction between pitch, intensity and rhythm.
  • Cutler, A. (1991). Prosody in situations of communication: Salience and segmentation. In Proceedings of the Twelfth International Congress of Phonetic Sciences: Vol. 1 (pp. 264-270). Aix-en-Provence: Université de Provence, Service des publications.

    Abstract

    Speakers and listeners have a shared goal: to communicate. The processes of speech perception and of speech production interact in many ways under the constraints of this communicative goal; such interaction is as characteristic of prosodic processing as of the processing of other aspects of linguistic structure. Two of the major uses of prosodic information in situations of communication are to encode salience and segmentation, and these themes unite the contributions to the symposium introduced by the present review.
  • Cutler, A., & Butterfield, S. (1986). The perceptual integrity of initial consonant clusters. In R. Lawrence (Ed.), Speech and Hearing: Proceedings of the Institute of Acoustics (pp. 31-36). Edinburgh: Institute of Acoustics.
  • Cutler, A., & Carter, D. (1987). The prosodic structure of initial syllables in English. In J. Laver, & M. Jack (Eds.), Proceedings of the European Conference on Speech Technology: Vol. 1 (pp. 207-210). Edinburgh: IEE.
  • Doherty, M., & Klein, W. (Eds.). (1991). Übersetzung [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (84).
  • 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.
  • Friederici, A., & Levelt, W. J. M. (1987). Spatial description in microgravity: Aspects of cognitive adaptation. In P. R. Sahm, R. Jansen, & M. Keller (Eds.), Proceedings of the Norderney Symposium on Scientific Results of the German Spacelab Mission D1 (pp. 518-524). Köln, Germany: Wissenschaftliche Projektführung DI c/o DFVLR.
  • 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.
  • 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.). (1976). Psycholinguistik [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (23/24).
  • Klein, W. (Ed.). (1987). Sprache und Ritual [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (65).
  • Klein, W. (Ed.). (1986). Sprachverfall [Special Issue]. Zeitschrift für Literaturwissenschaft und Linguistik, (62).
  • 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. (1991). Lexical access in speech production: Stages versus cascading. In H. Peters, W. Hulstijn, & C. Starkweather (Eds.), Speech motor control and stuttering (pp. 3-10). Amsterdam: Excerpta Medica.
  • Levelt, W. J. M., & Schriefers, H. (1987). Stages of lexical access. In G. A. Kempen (Ed.), Natural language generation: new results in artificial intelligence, psychology and linguistics (pp. 395-404). Dordrecht: Nijhoff.
  • Levinson, S. C. (1987). Minimization and conversational inference. In M. Bertuccelli Papi, & J. Verschueren (Eds.), The pragmatic perspective: Selected papers from the 1985 International Pragmatics Conference (pp. 61-129). Benjamins.
  • 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.
  • 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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  • 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.
  • 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.
  • Senft, G. (1991). Bakavilisi Biga - we can 'turn' the language - or: What happens to English words in Kilivila language? In W. Bahner, J. Schildt, & D. Viehwegger (Eds.), Proceedings of the XIVth International Congress of Linguists (pp. 1743-1746). Berlin: Akademie Verlag.
  • Seuren, P. A. M. (1991). Notes on noun phrases and quantification. In Proceedings of the International Conference on Current Issues in Computational Linguistics (pp. 19-44). Penang, Malaysia: Universiti Sains Malaysia.
  • Seuren, P. A. M. (1991). What makes a text untranslatable? In H. M. N. Noor Ein, & H. S. Atiah (Eds.), Pragmatik Penterjemahan: Prinsip, Amalan dan Penilaian Menuju ke Abad 21 ("The Pragmatics of Translation: Principles, Practice and Evaluation Moving towards the 21st Century") (pp. 19-27). Kuala Lumpur: Dewan Bahasa dan Pustaka.
  • 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.
  • 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.
  • 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.
  • Van Ooijen, B., Cutler, A., & Norris, D. (1991). Detection times for vowels versus consonants. In Eurospeech 91: Vol. 3 (pp. 1451-1454). Genova: Istituto Internazionale delle Comunicazioni.

    Abstract

    This paper reports two experiments with vowels and consonants as phoneme detection targets in real words. In the first experiment, two relatively distinct vowels were compared with two confusible stop consonants. Response times to the vowels were longer than to the consonants. Response times correlated negatively with target phoneme length. In the second, two relatively distinct vowels were compared with their corresponding semivowels. This time, the vowels were detected faster than the semivowels. We conclude that response time differences between vowels and stop consonants in this task may reflect differences between phoneme categories in the variability of tokens, both in the acoustic realisation of targets and in the' representation of targets by subjects.
  • Van Valin Jr., R. D. (1987). Aspects of the interaction of syntax and pragmatics: Discourse coreference mechanisms and the typology of grammatical systems. In M. Bertuccelli Papi, & J. Verschueren (Eds.), The pragmatic perspective: Selected papers from the 1985 International Pragmatics Conference (pp. 513-531). Amsterdam: Benjamins.
  • Van Valin Jr., R. D. (1987). Pragmatics, island phenomena, and linguistic competence. In A. M. Farley, P. T. Farley, & K.-E. McCullough (Eds.), CLS 22. Papers from the parasession on pragmatics and grammatical theory (pp. 223-233). Chicago Linguistic Society.
  • 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.
  • Vosse, T., & Kempen, G. (1991). A hybrid model of human sentence processing: Parsing right-branching, center-embedded and cross-serial dependencies. In M. Tomita (Ed.), Proceedings of the Second International Workshop on Parsing Technologies.
  • 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.

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  • 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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