Publications

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Journal and key conference articles

  1. Hupkes, D., Dankers, V., Mul, M., & Bruni, E. (preprint). The compositionality of neural networks: integrating symbolism and connectionism.
  2. Abnar, S., Beinborn, L., Choenni, R., & Zuidema, W. (2019). Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains. BlackboxNLP, ACL 2019.
  3. Abnar, S., Bedrax-Weiss, T., Kwiatkowski, T., & Cohen, W. W. (2019). Incremental Reading for Question Answering. International Journal of Computational Linguistics and Applications.
  4. Beinborn, L., Abnar, S., & Choenni, R. (2019). Robust Evaluation of Language-Brain Encoding Experiments. International Journal of Computational Linguistics and Applications.
  5. Jumelet, J., Zuidema, W., & Hupkes, D. (2019). Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment. ArXiv Preprint ArXiv:1909.08975.
  6. Zuidema, W., French, R. M., Alhama, R. G., Ellis, K., O’Donnell, T. J., Sainburg, T., & Gentner, T. Q. (2019). Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning. Topics in Cognitive Science. doi:10.1111/tops.12474
  7. Repplinger, M., Beinborn, L., & Zuidema, W. (2018). Vector-space models of words and sentences. Nieuw Archief Voor De Wiskunde.
  8. Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In Proceedings EMNLP workshop Analyzing and interpreting neural networks for NLP (BlackboxNLP).
  9. Hupkes, D., Veldhoen, S., & Zuidema, W. (2018). Visualisation and ‘Diagnostic Classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. Journal of Artificial Intelligence Research, 61, 907–926.
  10. Alhama, R. G., & Zuidema, W. (2018). Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules? Journal of Artificial Intelligence Research, 61, 927–946.
  11. Zuidema, W., & de Boer, B. (2018). The evolution of combinatorial structure in language. Current Opinion in Behavioral Sciences, 21, 138–144.
  12. van Woerkom, W., & Zuidema, W. (2017). Selecting the model that best fits the data - Commentary on Tali Leibovich, Naama Katzin, Maayan Harel and Avishai Henik, From ‘sense of number’ to ‘sense of magnitude’ – The role of continuous magnitudes in numerical cognition (in press). Behavioral and Brain Sciences, 40, e192.
  13. Le, P., & Zuidema, W. (2015). The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 1155–1164).
  14. Merker, B., Morley, I., & Zuidema, W. (2015). Five fundamental constraints on theories of the origins of music. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140095.
  15. Rohrmeier, M., Zuidema, W., Wiggins, G. A., & Scharff, C. (2015). Principles of structure building in music, language and animal song. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140097.
  16. Honing, H., & Zuidema, W. (2014). Decomposing dendrophilia. Physics of Life Reviews, 11, 375–376.
  17. Le, P., & Zuidema, W. (2014). The Inside-Outside Recursive Neural Network model for Dependency Parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 729–739).
  18. Beekhuizen, B., Bod, R., & Zuidema, W. (2013). Three Design Principles of Language: The Search for Parsimony in Redundancy. Language and Speech, 56, 265–290.
  19. Sangati, F., & Zuidema, W. (2011). Accurate parsing with compact tree-substitution grammars: Double-DOP. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 84–95).
  20. Zuidema, W., & Verhagen, A. (2010). What are the unique design features of language? Formal tools for comparative claims. Adaptive Behavior, 18, 48–65.
  21. Zuidema, W., & de Boer, B. (2009). The evolution of combinatorial phonology. Journal of Phonetics, 37, 125–144.
  22. Sangati, F., & Zuidema, W. (2009). Unsupervised methods for head assignments. In Proceedings of the European Chapter of the Association for Computational Linguistics (pp. 701–709).
  23. de Boer, B., & Zuidema, W. (2009). Models of Language Evolution : Does the Math Add Up ? ILLC Preprint Series, 1–10.
  24. Borensztajn, G., Zuidema, W., & Bod, R. (2009). Children’s Grammars Grow More Abstract with Age-Evidence from an Automatic Procedure for Identifying the Productive Units of Language. Topics in Cognitive Science, 1, 175–188.
  25. Borensztajn, G., Zuidema, W., & Bod, R. (2008). Taalverwerving: De wetenschap van de kleine wetenschapper. Kunst En Wetenschap, 17, 9–10.
  26. Zuidema, W., & de Boer, B. (2008). Evolutionary Explanations for Natural Language - Criteria from Evolutionary Biology. ILLC Preprint Series.
  27. Zuidema, W. (2007). Parsimonious Data-Oriented Parsing. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 551–560).
  28. Zuidema, W. (2003). How the Poverty of the Stimulus Solves the Poverty of the Stimulus. Advances in Neural Information Processing Systems 15 (Proceedings NIPS’02), 15, 51.
  29. Gardner, A., & Zuidema, W. (2003). Is Evolvability Involved in the Origin of Modular Variation? Evolution, 57, 1448–1450.
  30. Zuidema, W., & Westermann, G. (2003). Evolution of an Optimal Lexicon under Constraints from Embodiment. Artificial Life, 9, 387–402.
  31. Barton, N., & Zuidema, W. (2003). Evolution: The erratic path towards complexity. Current Biology, 13, 649–651.
  32. Zuidema, W. H., & de Boer, B. (2003). How did we get from there to here in the evolution of language? Behavioral and Brain Sciences, 26, 694–707.
  33. Zuidema, W. (2002). The importance of social learning in the evolution of cooperation and communication - Commentary on Howard Rachlin, Altruism and Selfishness. Behavioral and Brain Sciences, 25, 283–284.

Conference articles

  1. Baan, J., Leible, J., Nikolaus, M., Rau, D., Ulmer, D., Baumgärtner, T., … Bruni, E. (2019). On the Realization of Compositionality in Neural Networks. In BlackboxNLP, ACL 2019.
  2. Leonandya, R., Bruni, E., Hupkes, D., & Kruszewski, G. (2019). The Fast and the Flexible: training neural networks to learn to follow instructions from small data. In The 13th International Conference on Computational Semantics (IWCS).
  3. Hupkes, D., Singh, A., Korrel, K., Kruszewski, G., & Bruni, E. (2019). Learning compositionally through attentive guidance. In 20th International Conference on Computational Linguistics and Intelligent Text Processing.
  4. Ulmer, D., Hupkes, D., & Bruni, E. (2019). Assessing incrementality in sequence-to-sequence models. In Repl4NLP, ACL 2019.
  5. Lakretz, Y., Kruszewski, G., Desbordes, T., Hupkes, D., Dehaene, S., & Baroni, M. (2019). The emergence of number and syntax units in LSTM language models. In NAACL 2019.
  6. Jumelet, J., & Hupkes, D. (2018). Do language models understand anything? On the ability of LSTMs to understand negative polarity items. In BlackboxNLP 2018, ACL.
  7. Abnar, S., Ahmed, R., Mijnheer, M., & Zuidema, W. (2018). Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity (preprint). In Proceedings Workshop on Cognitive Modeling and Computational Linguistics (CMCL). doi:10.3389/conf.fninf.2014.18.00084
  8. Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. (2018). Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information. In Proceedings EMNLP workshop Analyzing and interpreting neural networks for NLP (BlackboxNLP).
  9. Hupkes, D., Bouwmeester, S., & Fernández, R. (2018). Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue. In BlackboxNLP 2018, ACL.
  10. Hupkes, D., & Zuidema, W. (2017). Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks. In Workshop on Interpreting, Explaining and Visualizing Deep Learning (at NIPS).
  11. Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition : the R & R model. In Proceedings of the Conference of the Cognitive Science Society.
  12. Veldhoen, S., & Zuidema, W. (2017). Can Neural Networks learn Logical Reasoning? In Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML) (pp. pp. 35–41). University of Gothenburgh, Sweden.
  13. Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. In Proceeding Cognitive Aspects of Computational Language Learning (Workshop at ACL) (pp. 64–72).
  14. Le, P., & Zuidema, W. (2016). Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs. In Workshop on Representation Learning (at ACL). doi:10.18653/v1/W16-1610
  15. Hupkes, D., & Bod, R. (2016). POS-tagging of Historical Dutch. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC’16) (pp. 77–82).
  16. Veldhoen, S., Hupkes, D., & Zuidema, W. (2016). Diagnostic classifiers: revealing how neural networks process hierarchical structure. In Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (at NIPS).
  17. Le, P., & Zuidema, W. (2015). Unsupervised Dependency Parsing: Let’s Use Supervised Parsers. In Proceedings NAACL.
  18. Monaghan, P., & Zuidema, W. H. (2015). General purpose cognitive processing constraints and phonotactic propoerties of the vocabulary. In Proceedings of the International Conference of the Phonetic Sciences (ICPhS).
  19. Le, P., & Zuidema, W. (2015). Compositional Distributional Semantics with Long Short Term Memory. In Proceedings *SEM. doi:10.18653/v1/S15-1002
  20. Le, P., & Zuidema, W. (2015). The Forest Convolutional Network : Compositional Distributional Semantics with a Neural Chart and without Binarization. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 1155–1164).
  21. Zuidema, W. (2014). Requirements on Scenarios for the Evolution of Language and Cognition. In Proceedings of the International Conference on the Evolution of Language (pp. 565–566). World Scientific.
  22. Le, P., & Zuidema, W. (2014). Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition. In Workshop on Deep Neural Networks and Representation Learning (at NIPS) (pp. 1–11).
  23. Le, P., & Zuidema, W. (2014). The Inside-Outside Recursive Neural Network model for Dependency Parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 729–739).
  24. Woensdregt, M. S., & Zuidema, W. (2014). Neural Networks, Algebraic Rules and Human Uniqueness. In Proceedings of the International Conference on the Evolution of Language (pp. 561–562). World Scientific.
  25. Alhama, R. G., Scha, R., & Zuidema, W. (2014). Rule Learning in Humans and Animals. In Proceedings of the International Conference on the Evolution of Language (pp. 371–372). World Scientific.
  26. Le, P., Zuidema, W., & Scha, R. (2013). Learning from errors: Using vector-based compositional semantics for parse reranking. In Proceedings Workshop on Continuous Vector Space Models and their Compositionality (at ACL 2013).
  27. Zuidema, W. (2013). Contextfreeness Revisited. In Proceedings of the Conference of the Cognitive Science Society (pp. 1664–1669).
  28. Le, P., & Zuidema, W. (2012). Learning Compositional Semantics for Open Domain Semantic Parsing. In Proceedings COLING (pp. 1535–1552). Mumbai.
  29. Kunert, R., Fernández, R., & Zuidema, W. (2011). Adaptation in child directed speech: Evidence from corpora. In Proceedings of the Workshop on the Semantics and Pragmatics of Dialogue (pp. 112–119).
  30. Sangati, F., & Zuidema, W. (2011). Accurate parsing with compact tree-substitution grammars: Double-DOP. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 84–95).
  31. Borensztajn, G., & Zuidema, W. (2011). Episodic grammar : a computational model of the interaction between episodic and semantic memory in language processing. In Proceedings of the Conference of the Cognitive Science Society (pp. 507–512).
  32. Alhama, R. G., & Scha, R. (2011). How should we Evaluate Models of Segmentation in Artificial Language Learning. In Proceedings of the International Conference on Cognitive Modeling (pp. 172–173).
  33. de Boer, B., & Zuidema, W. (2010). Multi-agent simulations of the evolution of combinatorial phonology. In Proceedings of the Belgian/Netherlands Artificial Intelligence Conference (Vol. 18, pp. 66–73).
  34. Sangati, F., Zuidema, W., & Bod, R. (2010). Efficiently extract recurring tree fragments from large treebanks. In Proceedings LREC (pp. 219–226).
  35. Ferdinand, V., & Zuidema, W. (2009). Thomas ’ theorem meets Bayes ’ rule : a model of the iterated learning of language. In Proceedings of the Conference of the Cognitive Science Society.
  36. Borensztajn, G., Zuidema, W., & Bod, R. (2009). The hierarchical prediction network: towards a neural theory of grammar acquisition. In Proceedings of the Conference of the Cognitive Science Society (pp. 2974–2979).
  37. Sangati, F., & Zuidema, W. (2009). Unsupervised methods for head assignments. In Proceedings of the European Chapter of the Association for Computational Linguistics (pp. 701–709).
  38. van Heijningen, C. A. A., de Visser, J., Zuidema, W., & ten Cate, C. (2009). Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species. In Proceedings of the National Academy of Sciences (Vol. 106, pp. 20538–20543).
  39. Sangati, F., Zuidema, W., & Bod, R. (2009). A generative re-ranking model for dependency parsing. In Proceedings of the International Conference on Parsing Technologies (IWPT) (pp. 238–241).
  40. Zuidema, W. (2008). A Gradual Path To Hierarchical Phrase-Structure: Insights from Modeling and Corpus-Data. In A. D. M. Smith, K. Smith, & R. Ferrer i Cancho (Eds.), Proceedings of the International Conference on the Evolution of Language (pp. 509–510). World Scientific.
  41. Sangati, F., & Zuidema, W. (2008). Communication, cooperation and coherence. In A. D. M. Smith, K. Smith, & R. Ferrer i Cancho (Eds.), Proceedings of the International Conference on the Evolution of Language. World Scientific.
  42. Zuidema, W. (2007). Parsimonious Data-Oriented Parsing. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 551–560).
  43. Zuidema, W. (2006). Theoretical Evaluation of Estimation Methods for Data-Oriented Parsing. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL) (pp. 1–4).
  44. Zuidema, W., & O’Donnell, T. J. (2006). Beyond the argument from design. In Proceedings of the International Conference on the Evolution of Language. doi:10.1142/9789812774262
  45. Zuidema, W. (2006). What are the productive units of natural language grammar?: a DOP approach to the automatic identification of constructions. In Proceedings of the Conference on Computational Natural Language Learning (CoNLL) (pp. 29–36).
  46. Zuidema, W. (2003). Optimal Communication in a Noisy and Heterogeneous Environment. In Proceedings of the European Conference on Artificial Life (pp. 651–658).
  47. Beule, J. D., van Looveren, J., & Zuidema, W. (2002). Grounding Formal Syntax in an Almost Real World. In Proceedings of the Belgian/Netherlands Artificial Intelligence Conference.
  48. Zuidema, W. (2002). Language adaptation helps language acquisition. In B. Hallam, D. Floreano, J. Hallam, G. Hayes, & J.-A. Meye (Eds.), Proceedings of the International Conference on Simulation of Adaptive Behavior (Vol. 7, pp. 417–418). MIT Press.
  49. Zuidema, W., & Westermann, G. (2001). Towards formal models of embodiment and self-organization of language. In Proceedings of the Workshop on Developmental Embodied Cognition at the Annual Meeting of the Cognitive Science Society.
  50. Zuidema, W. H. (2001). Emergent syntax: The unremitting value of computational modeling for understanding the origins of complex language. In Proceedings of the European Conference on Artificial Life (pp. 641–644).
  51. Zuidema, W., & Hogeweg, P. (2000). Social patterns guide evolving grammars. In Proceedings of the International Conference on the Evolution of Language.
  52. Zuidema, W., & Hogeberg, P. (2000). Selective advantages of syntactic language - a model study. In Proceedings of the Annual Meeting of the Cognitive Science Society.

Book chapters

  1. Zuidema, W., & Le, P. (2019). Vector-based and Neural Models of Semantics. In P. Hagoort (Ed.), Human Language. MIT press.
  2. Zuidema, W., & Fitz, H. (2019). Models of human language and speech processing. In P. Hagoort (Ed.), Human Language. MIT press.
  3. Merker, B., Morley, I., & Zuidema, W. (2018). Five fundamental constraints on theories of the origins of music. In H. J. Honing (Ed.), The Origins of Musicality (pp. 49–80). MIT Press.
  4. Zuidema, W., Hupkes, D., Wiggins, G. A., Scharff, C., & Rohrmeirer, M. (2018). Formal Models of Structure Building in Music, Language, and Animal Song. In H. J. Honing (Ed.), The Origins of Musicality (pp. 253–286). MIT Press.
  5. Zuidema, J. (2015). Zwarte inktvlekjes op een witte achtergrond. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën – Dit is het mooiste ooit. Maven.
  6. Borensztajn, G., Zuidema, W., & Bechtel, W. (2014). Systematicity and the Need for Encapsulated Representations. In P. Calvo & J. Symons (Eds.), The Architecture of Cognition (p. 165). MIT Press.
  7. Zuidema, J. (2014). Relax – Taalfouten bestaan niet. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën – Dit wil je weten (pp. 256–258). Maven.
  8. Zuidema, W., & De Boer, B. (2013). Modeling in the Language Sciences. In R. J. Podesva & D. Sharma (Eds.), Research Methods in Linguistics (pp. 422–439). Cambridge University Press.
  9. ten Cate, C., Lachlan, R., & Zuidema, W. (2013). Analyzing the Structure of Bird Vocalizations and Language: Finding Common Ground. In J. J. Bolhuis & M. Everaert (Eds.), Birdsong, Speech, and Language (pp. 243–260). MIT Press.
  10. Zuidema, J. (2013). Honderdduizend jaar nuttig geklets. In M. Geels & T. van Opijnen (Eds.), Nederland in ideeën: 101 denkers over inzichten en innovaties die ons land verander(d)en. Maven.
  11. Zuidema, W. (2013). Language in Nature: On the Evolutionary Roots of a Cultural Phenomenon. In B. Philippe & S. Kenny (Eds.), The Language Phenomenon (pp. 163–189). Springer.
  12. Zuidema, J. (2013). Van A naar B. In A. Reuneker, R. Boogaart, & S. Lensink (Eds.), Aries netwerk – een constructicon. Columns aangeboden aan Arie Verhagen.
  13. Versteegh, M., Sangati, F., & Zuidema, W. (2010). Simulations Of Socio-Linguistic Change: Implications for Unidirectionality. In Proceedings of the International Conference on the Evolution of Language (pp. 511–512). World Scientific.

Others

  1. de Gooijer, S. (2016). Task-Based Semantic Evaluation of Parse Tree Discontinuity - Master’s thesis (Master's thesis).
  2. Ferdinand, V. (2008). An experiment in iterated function learning. ILLC Preprint Series.
  3. Ferdinand, V., & Zuidema, W. (2008). Language adapting to the brain: a study of a Bayesian iterated learning model. ILLC Preprint Series.
  4. Borensztajn, G., & Zuidema, W. (2007). Bayesian Model Merging for Unsupervised Constituent Labeling and Grammar Induction. ILLC Prepublication.
  5. Zuidema, W. H. (2005). The Major Transitions in the Evolution of Language (PhD thesis).
  6. Zuidema, W., & Westermann, G. (2001). On the Relevance of Language Evolution Models for Cognitive Science. AI-MEMO.
  7. Zuidema, W. H. (2000). Evolution of syntax in groups of agents - Master’s thesis (Master's thesis).