COMPUTATIONAL PSYCHOLINGUISTICS LAB

@ JOHNS HOPKINS UNIVERSITY


What are the mental representations that constitute our knowledge of language? How do we use them to understand and produce language? In the Computational Psycholinguistics Lab, we address these questions and others through the use of computational models and human experiments. Our lab is part of the Department of Cognitive Science at Johns Hopkins University, and we frequently collaborate with the Center for Language and Speech Processing. Read on to learn more about who we are and what we do.


LAB NEWS


  • Tal gave LTI colloquium talk at Carnegie Mellon University (February 8, 2019)
  • Marty gave a presentation at SCiL (January 4, 2019)
  • Tal gave talks over winter break at Yale Linguistics (December 10), Microsoft Research Redmond (December 13), Allen AI Institue (December 14), and Google New York (January 7). (December 2018 - January 2019)
  • Paper on interpreting neural network internal representations using tensor products accepted to ICLR (December 2018)
  • paper and extended abstract accepted to the Society for COmputation in Linguistics. (November 2018)
  • Tal Co-organized the workshop on analyzing and interpreting neural networks for NLP (at EMNLP). (November 1, 2018)
  • Tal visited the University of Potsdam. (September 3-4, 2018)
  • Marty and Suhas both had presentations accepted at the upcoming AMLaP conference in Berlin. (August 18, 2018)
  • Becky and Marty both had papers accepted to EMNLP in Brussels. (August 10, 2018)
  • Tal gave a Linguistics colloquium talk at Tel Aviv University. (June 7, 2018)
  • Tal gave a Linguistics colloquium talk at Bar-Ilan University. (May 29, 2018)
  • Paper on acceptability judgments accepted to Glossa. (May 2018)
  • Three papers accepted to the Cognitive Science Society conference: 123. (May 2018)
  • Marty gave a talk and Becky presented a poster at the 2018 Mid-Atlantic Student Colloquium on Speech, Language, and Learning (May 12, 2018)
  • Tal gave a CLIP/LING colloquium talk at the University of Maryland (April 11, 2018)
  • Suhas accepted the Cognitive Science Department's offer of admission to the PhD program, with Tal as his prospective advisor. (April 1, 2018)
  • Tom received a National Science Foundation Graduate Research Fellowship (April 3, 2017)
  • Tal presented his and Brian's Poster on agreement attraction and time pressure and Grusha presented her poster on singular 'they' at the CUNY sentence processing conference. (March 15-17, 2017)
  • Paper using nonce sentences for syntactic evaluation of RNNs across languages (with Facebook AI Research collaborators) to appear at NAACL. (March 2018)
  • Tal gave a talk at the Common Ground Seminar at UPenn. (February 7, 2018)
  • Tom presented a poster and gave a talk on his work with Robert Frank at the Society for Computation in Linguistics Conference (January 4-7, 2018)
  • Tal is co-organizing a Workshop on Analyzing and Interpreting Neural Networks for NLP at EMNLP in November 2018 (with Afra Alishahi and Grzegorz Chrupala). (2017)
  • Paper accepted to the 2018 Cognitive Modeling and Computational Linguistics workshop. (October 2017)
  • Tal gave a colloquium talk at the linguistics department at Stony Brook University. (September 29, 2017)
  • Tal participated in the Meaning in Context workshop at Stanford. (September 12-15, 2017)
  • Tal gave a talk at the workshop on Deep Learning in Computational Cognitive Science at the Annual Meeting of the Cognitive Science Society. (July 26, 2017)

PEOPLE



PRINCIPAL INVESTIGATOR



Tal Linzen ( personal site ) 
Assistant Professor of Cognitive Science


Tal is an Assistant Professor in the Department of Cognitive Science at Johns Hopkins University where he directs the JHU Computational Psycholinguistics Lab. He is also affiliated with the Center for Language and Speech Processing



POST-DOCS


Marten van Schijndel ( personal site ) 
Post-Doctoral Fellow


I'm interested in incremental (left-to-right, single pass) neural language models. I analyze the linguistic representations learned by these models to see what linguistic aspects they find helpful, and I test their cognitive plausibility by evaluating how well their performance matches human behavior (e.g. reading times or speech errors).




GRADUATE STUDENTS


Grusha Prasad ( personal site ) 
PhD Student in Cognitive Science


I am interested in how people represent statistical regularities in linguistic structure and what factors can cause these representations to change. My approach to addressing these questions involves running psycholinguistic experiments on humans that are informed by computational models of the linguistic/cognitive phenomenon of interest. Outside of work, puns and word play get me very excited.


Tom McCoy ( personal site ) 
PhD Student in Cognitive Science


I use computational modeling to understand the formal properties of language, how these properties are instantiated in the mind, and which of these properties are innate vs. learned. I am currently co-advised by Tal Linzen and Paul Smolensky, and I continue to collaborate with my undergraduate advisor, Robert Frank. Outside of research, I enjoy running and constructing crossword puzzles.


Suhas Arehalli ( personal site ) 
PhD Student in Cognitive Science


My interests include machine learning, computational modelling, and psycholinguistics. I am particularly interested in the cognitive mechanisms underlying sentence processing, and particularly in what linguistic illusions can tell us about them. I am also passionate about teaching statistical and computational literacy, particularly how algorithms can think about data and the impact on society of those algorithms.




AFFILIATED GRADUATE STUDENTS


Rebecca Marvin ( personal site ) 
PhD Student in Computer Science


I'm working on my PhD in the Center for Language and Speech Processing in the Computer Science Department at JHU. My main research interests include machine translation, error analysis, and interpretability of neural systems. When I'm not working, I'm probably spending time with my cat.




Undergraduate Research Assistants


Daniela Torres 
Sophomore in Cognitive Science



Meg Obata 
Senior in Cognitive Science w/ Marketing and Communications minors





LAB MANAGER


Brian Leonard 
Lab Manager


I'm interested in everything for which science doesn't have clear answers yet. Naturally, this makes language an ideal field of study. I'm particularly interested in the question of how syntactic structure is generated and processed in the mind. I spend my free time telling jokes, reading fiction, and writing poems.





RESEARCH



OVERVIEW


What are the mental representations that constitute our knowledge of language? How do we use them to understand and produce language?

We address these questions using computational models and human experiments. The goal of our models is to mimic the processes that humans engage in when learning and processing language; these models often combine techniques from machine learning with representations from theoretical linguistics.

We then compare the predictions of these models to human language comprehension. In a typical experiment in our lab, we invite participants to read a range of sentences, and record how long they take to read each word, measured based on key presses or eye movements. Other techniques include artificial language learning experiments and neural measurements.

Finally, we use linguistics and psycholinguistics to understand and improve artificial intelligence systems, in particular “deep learning” models that are otherwise difficult to analyze.


EXPECTATION-BASED LANGUAGE COMPREHENSION


The probability of a word or a syntactic structure is a major predictor of how difficult they are to read. What are the syntactic representations over which those probability distributions are maintained? How is processing difficulty affected by the probability distribution we maintain over the representations we predict, and in particular, our uncertainty about the structure and meaning of the sentence?

We can study these questions by implementing computational models that which incorporate different representational assumptions, and deriving quantitative predictions from those models:

We can then measure to what extent these predictions match up with human sentence comprehension processes, as measured by reading times (eyetracking, self-paced reading) or neural measurements such as MEG.

Expectations are sometimes malleable and context-specific. If the person we’re talking to is unusually fond of a particular syntactic construction, say passive verbs, we might learn to expect them to use this construction more often than other people. In ongoing research, we’re investigating the extent to which our expectations for specific syntactic representations can vary from context to context.


LINGUISTIC REPRESENTATIONS IN ARTIFICIAL NEURAL NETWORKS


Artificial neural networks are a powerful statistical learning technique that underpins some of the best-performing artificial intelligence software we have. Many of the neural networks that have been successful in practical applications do not have any explicit linguistic representations (e.g., syntax trees or logical forms). Is the performance of neural networks really as impressive when evaluated using rigorous linguistic and psycholinguistic tests? If so, how do these networks represent or approximate the structures that are normally seen as the building blocks of language?

A related topic of research is lexical representations in neural networks. Neural networks are typically allowed to evolve their own lexical representations, which are normally nothing but unstructured lists of numbers. We have explored to what extent such lexical representations implicitly capture the linguistic distinctions that are assumed in linguistics (in particular, formal semantics).


GENERALIZATION IN LANGUAGE


We regularly generalize our knowledge of language to words and sentences we have never heard before. When is our linguistic knowledge limited to a specific item, and when do we apply it to novel items? What representations do we use to generalize beyond the specific items that we have encountered?

We can often study these questions using artificial language learning experiments. In one experiment, for example, we taught participants an artificial language with a simple phonological regularity, and tested how they generalized this regularity to new sounds:


PUBLICATIONS



IN PROGRESS


    • RNNs Implicitly Implement Tensor Product Representations. R. T. McCoy, T. Linzen, E. Dunbar, & P. Smolensky. (2019). To Appear in International Conference on Learning Representations (ICLR). [Abstract] [PDF] [BibTeX]

    • What can linguistics and deep learning contribute to each other?. T. Linzen. (2018). To Appear in Language. [Abstract] [PDF] [BibTeX]

    • Syntactic categories as lexical features or syntactic heads: An MEG approach. J. King, T. Linzen, & A. (accepted with revisions) Marantz. (2015). Linguistic Inquiry. [Abstract] [PDF] [BibTeX]

    • Syntactic categories as lexical features or syntactic heads: An MEG approach. J. King, T. Linzen, & A. Marantz. n.d. Linguistic Inquiry. [Abstract] [PDF] [BibTeX]



PUBLISHED


2019

    • Non-entailed subsequences as a challenge for natural language inference. R. T. McCoy, & T. Linzen. (2019). In Proceedings of the Society for Computation in Linguistics (SCiL). [PDF] [BibTeX]

    • Can Entropy Explain Successor Surprisal Effects in Reading?. M. van Schijndel, & T. Linzen. (2019). In Proceedings of the Society for Computation in Linguistics (SCiL). [Abstract] [PDF] [BibTeX]

2018

    • Colorless green recurrent networks dream hierarchically. K. Gulordava, P. Bojanowski, E. Grave, T. Linzen, & M. Baroni. (2018). CoRR. [Abstract] [PDF] [BibTeX]

    • A morphosyntactic inductive bias in artificial language learning. I. Kastner, & T. Linzen. (2018). [PDF] [BibTeX]

    • A morphosyntactic inductive bias in artificial language learning.. J. White, R. Kager, T. Linzen, G. Markopoulos, A. Martin, A. Nevins, S. Peperkamp, K. Polgárdi, N. Topintzi, & R. van de Vijver. (2018). In the 48th Annual Meeting of the North East Linguistic Society (NELS 48). [PDF] [BibTeX]

    • Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks. R. T. McCoy, R. Frank, & T. Linzen. (2018). In Proceedings of the 40th Annual Conference of the Cognitive Science Society. [Abstract] [PDF] [BibTeX]

    • The reliability of acceptability judgments across languages. Tal Linzen & Yohei Oseki. (2018). Glossa: a Journal of General Linguistics. [Abstract] [PDF] [BibTeX]

    • Modeling garden path effects without explicit hierarchical syntax. M. van Schijndel, & T. Linzen. (2018). In Proceedings of the 40th Annual Conference of the Cognitive Science Society. [Abstract] [PDF] [BibTeX]

    • Distinct patterns of syntactic agreement errors in recurrent networks and humans. T. Linzen, & B. Leonard. (2018). In Proceedings of the 40th Annual Conference of the Cognitive Science Society. [Abstract] [PDF] [BibTeX]

    • In spoken word recognition the future predicts the past. L. Gwilliams, T. Linzen, D. Poeppel, & A. Marantz. (2018). Journal of Neuroscience. [Abstract] [PDF] [BibTeX]

    • Targeted syntactic evaluation of language models. R. Martin, & T. Linzen. (2018). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018). [Abstract] [PDF] [BibTeX]

    • A neural model of adaptation in reading. M. van Schijndel, & T. Linzen. (2018). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018). [Abstract] [PDF] [BibTeX]

    • Phonological (un)certainty weights lexical activation. L. Gwilliams, D. Poeppel, A. Marantz, & T. Linzen. (2018). In Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018). [Abstract] [PDF] [BibTeX]

    • Preference for locality is affected by the prefix/suffix asymmetry: Evidence from artificial language learning. J. White, R. Kager, T. Linzen, G. Markopoulos, A. Martin, A. Nevins, S. Peperkamp, K. Polgárdi, N. Topintzi, & R. van de Vijver. (2018). In the 48th Annual Meeting of the North East Linguistic Society (NELS 48). [PDF] [BibTeX]

2017

    • Prediction and uncertainty in an artificial language. T. Linzen, N. Siegelman, & L. Bogaerts. (2017). In Proceedings of the 39th Annual Conference of the Cognitive Science Society. [Abstract] [PDF] [BibTeX]

    • Exploring the Syntactic Abilities of RNNs with Multi-task Learning. É. Enguehard, Y. Goldberg, & T. Linzen. (2017). In Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL). [Abstract] [PDF] [BibTeX]

    • Rapid generalization in phonotactic learning. G. Gallagher, & T. Linzen. (2017). Laboratory Phonology. [Abstract] [PDF] [BibTeX]

    • Comparing Character-level Neural Language Models Using a Lexical Decision Task. G. Le Godais, T. Linzen, & E. Dupoux. (2017). In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. [Abstract] [PDF] [BibTeX]

2016

    • Evaluating vector space models using human semantic priming results. A. Ettinger, & T. Linzen. (2016). In Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP. [Abstract] [PDF] [BibTeX]

    • Against all odds: exhaustive activation in lexical access of verb complementation options. E. Shetreet, T. Linzen, & N. Friedmann. (2016). Language, Cognition and Neuroscience. [Abstract] [PDF] [BibTeX]

    • Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions. T. Linzen, & T. F. Jaeger. (2016). Cognitive Science. [Abstract] [PDF] [BibTeX]

    • The diminishing role of inalienability in the {Hebrew} possessive dative. T. Linzen. (2016). Corpus Linguistics and Linguistic Theory. [Abstract] [PDF] [BibTeX]

    • Quantificational features in distributional word representations. T. Linzen, E. Dupoux, & B. Spector. (2016). In Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics (*SEM 2016). [Abstract] [PDF] [BibTeX]

    • Issues in evaluating semantic spaces using word analogies. T. Linzen. (2016). In Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP. [Abstract] [PDF] [BibTeX]

    • Assessing the ability of {LSTMs} to learn syntax-sensitive dependencies. T. Linzen, E. Dupoux, & Y. Goldberg. (2016). Transactions of the Association for Computational Linguistics. [Abstract] [PDF] [BibTeX]

2015

    • Morphological conditioning of phonological regularization. M. Gouskova, & T. Linzen. (2015). The Linguistic Review. [Abstract] [PDF] [BibTeX]

    • Pronominal datives: The royal road to argument status. M. Ariel, E. Dattner, J. W. Du Bois, & T. Linzen. (2015). Studies in Language. [Abstract] [PDF] [BibTeX]

    • Lexical preactivation in basic linguistic phrases. J. Fruchter, T. Linzen, M. Westerlund, & A. Marantz. (2015). Journal of Cognitive Neuroscience. [Abstract] [PDF] [BibTeX]

    • A model of rapid phonotactic generalization. T. Linzen, & T. J. O’Donnell. (2015). In Proceedings of Empirical Methods for Natural Language Processing (EMNLP) 2015. [Abstract] [PDF] [BibTeX]

2014

    • The role of morphology in phoneme prediction: Evidence from MEG. A. Ettinger, T. Linzen, & A. Marantz. (2014). Brain and Language. [Abstract] [PDF] [BibTeX]

    • Parallels between cross-linguistic and language-internal variation in Hebrew possessive constructions. T. Linzen. (2014). Linguistics. [Abstract] [PDF] [BibTeX]

    • The timecourse of generalization in phonotactic learning. T. Linzen, & G. Gallagher. (2014). In Proceedings of Phonology 2013 , J. Kingston, C. Moore-Cantwell, J. Pater, & R. Staub (Editors). [PDF] [BibTeX]

    • Investigating the role of entropy in sentence processing. T. Linzen, & T. F. Jaeger. (2014). In Proceedings of the 2014 ACL Workshop on Cognitive Modeling and Computational Linguistics. [Abstract] [PDF] [BibTeX]

2013

    • Syntactic context effects in visual word recognition: An {MEG} study. T. Linzen, A. Marantz, & L. Pylkkänen. (2013). The Mental Lexicon. [Abstract] [PDF] [BibTeX]

    • Lexical and phonological variation in {Russian} prepositions. T. Linzen, S. Kasyanenko, & M. Gouskova. (2013). Phonology. [Abstract] [PDF] [BibTeX]