Students and faculty are currently working on diverse projects in computational phonetics, phonology, syntax, and semantics.
Neurolinguistics & Psycholinguistics
John Hale • Shohini Bhattasali • Jixing Li
Using various different models from computational linguistics, we study the cognitive neuroscience of language. Through naturalistic speech comprehension data from fMRI studies, we are investigating different linguistic questions such as comparing compositional meaning to frozen meaning, semantic coherence vs. incoherence, binding theory & pronoun resolution among other topics.
Jacob Collard • John Hale • Mats Rooth
This project explores the learnability of various syntactic formalisms such as Categorial Grammars and Dependency Grammars and attempts to provide a more naturalistic algorithm for learning syntax that does not rely on extensively annotated structures, but rather on inferences that can be made from basic knowledge. We are also investigating how learned grammars compare to engineered grammars and to claims made in theoretical syntax.
Mats Rooth • Simone Harmath-de Lemos • Shohini Bhattasali
In this project we train a finite state model to detect prosodic cues in a speech corpus. We are specifically interested in detecting stress cues in Brazilian Portuguese and Bengali and finding empirical evidence for current theoretical views.
Forrest Davis and Gerry T.M. Altmann. (2021), Finding Event Structure in Time: What Recurrent Neural Networks can tell us about Event Structure in Mind
Cognition, 213: 104651, 2021
Marten van Schijndel and Tal Linzen. (2021), Single-stage prediction models do not explain the magnitude of syntactic disambiguation difficulty
Cognitive Science, 45(6): e12988, 2021
Simone Harmath-de Lemos. (2021), Detecting word-level stress in continuous speech: A case study of Brazilian Portuguese
Journal of Portuguese Linguistics 20.1, 2021
Eric Campbell and Mats Rooth. (2021), Epistemic semantics in guarded string models
Proceedings of the Society for Computation in Linguistics (SCiL).2021
William Timkey and Marten van Schijndel. (2021), All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2021
Forrest Davis and Marten van Schijndel. (2021), Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning
In Proceedings of the 2021 Annual Conference of the Association for Computational Linguistics (ACL). 2021
Matt Wilber, William Timkey, and Marten van Schijndel. (2021), Understanding How Abstractive Summarizers Paraphrase Text
Proceedings of the 2021 Findings of the ACL. 2021
Samuel Ryb and Marten van Schijndel. (2021), Analytical, Symbolic and First-Order Reasoning within Neural Architectures
Proceedings of the 2021 Workshop on Computing Semantics with Types, Frames and Related Structures. 2021.
Cory Shain, Idan Blank, Marten van Schijndel. (2020), William Schuler, and Evelina Fedorenko. (2020) fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.
Neuropsychologia, 138:107307. 2020
Forrest Davis and Marten van Schijndel. (2020) Recurrent neural network language models always learn English-like relative clause attachment.
Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics (ACL). 2020.
Forrest Davis and Marten van Schijndel. (2020) Discourse structure interacts with reference but not syntax in neural language models.
24th Conference on Computational Natural Language Learning (CoNLL). 2020.
Debasmita Bhattacharya and Marten van Schijndel. (2020) Filler-gaps that neural networks fail to generalize.
24th Conference on Computational Natural Language Learning (CoNLL). 2020.
Forrest Davis and Marten van Schijndel. (2020) Interaction with context during recurrent neural network sentence processing.
Proceedings of the 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci). 2020.
Publications prior to 2020
Marten van Schijndel, Aaron Mueller, and Tal Linzen. (2019) Quantity doesn't buy quality syntax with neural language models.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCAI). 2019.
Grusha Prasad, Marten van Schijndel, and Tal Linzen. (2019) Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models.
Proceedings of the 2019 Conference on Computational Natural Language Learning (CoNLL). 2019.
Forrest Davis and Abby Cohn. (2019) Effects of lexical frequency and compositionality on phonological reduction in English compounds.
25th Architectures and Mechanisms of Language Processing conference (AMLaP 2019)
Jacob Collard. (2018) Finite State Reasoning for Presupposition Satisfaction.
Proceedings of the First International Workshop on Language Cognition and Computational Models (COLING 2018)
Shohini Bhattasali, Murielle Fabre, John Hale. (2018) Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs.
Proceedings of the 14th Workshop on Multiword Expressions (COLING 2018)
Simone Harmath-de Lemos. (2018) What Automatic Speech Recognition Can Tell Us About Stress and Stress Shift in Continuous Speech.
Proceedings of the 9th International Conference on Speech Prosody 2018
Jixing Li, Murielle Fabre, Wen-Ming Luh, John Hale. (2018) Modeling Brain Activity Associated with Pronoun Resolution in English and Chinese.
Proceedings of NAACL Workshop on Computational Models of Reference, Anaphora, and Coreference (CRAC 2018)
Jacob Collard. (2018) A Naturalistic Inference Learning Algorithm.
Linguistic Society of America (LSA 2018)
Shohini Bhattasali, John Hale, Christophe Pallier, Jonathan R. Brennan, Wen-Ming Luh, R. Nathan Spreng. (2018) Differentiating Phrase Structure Parsing and Memory Retrieval in the Brain.
Proceedings of the Society for Computation in Linguistics (SCiL 2018)
Mats Rooth. (2017) Finite-state intensional semantics.
12th International Conference on Computational Semantics (IWCS 2017)
- Matthew Nelson, Imen El Karoui, Kristof Giber, Xiaofang Yang, Laurent Cohen, Hilda Koopman, Sydney S. Cash, Lionel Naccache, John Hale, Christophe Pallier, Stanislas Dehaune. (2017) Neurophysiological dynamics of phrase-structure building during sentence processing.
Proceedings of the National Academy of Sciences
- Matthew Nelson, Stanislas Dehaene, Christophe Pallier, and John Hale. (2017). Entropy Reduction correlates with Temporal Lobe Activity.
Proceedings of the 7th Workshop on Cognitive Modelling and Computational Linguistics (CMCL 2017)
Jacob Collard. (2016) Inferring Necessary Categories in CCG.
9th International Conference on the Logical Aspects of Computational Linguistics (LACL 2016)
- Jonathan Howell, Mats Rooth, and Michael Wagner. (2016). Acoustic classification of focus: on the web and in the lab. Doi 1813/42538
- Jonathan R. Brennan, Edward P. Stabler, Sarah E. Van Wagenen, Wen-Ming Luh, and John T. Hale. (2016) Abstract linguistic structure correlates with temporal activity during naturalistic comprehension. Brain and Language 157, 81-94.
John Hale. (2016). Information-theoretical complexity metrics."
Language and Linguistic Compass
Jixing Li, Jonathan Brennan, Adam Mahar, and John Hale. (2016). Temporal lobes as combinatory engines for both form and meaning.
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC 2016)
John T. Hale, David E. Lutz, Wenming Luh, and Jonathan R. Brennan. (2015). Modeling fMRI time courses with linguistic structure at various grain sizes.
Proceedings of CMCL 2015
Shohini Bhattasali, Jeremy Cytryn, Elana Feldman, and Joonsuk Park. (2015). Automatic identification of rhetorical questions.
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015)
LING 4424: Computational Linguistics
Introduction to computational linguistics. Possible topics include syntactic parsing
using functional programming, logic-based computational semantics, and finite state
modeling of phonology and phonetics.
LING 4434: Computational Linguistics 2
Computational Linguistics 2 - This course introduces techniques to probe for linguistic representations in neural network models of language. Centered around discussion of current research papers as well as student research projects.
LING 4429/6429: Grammar Formalisms
This course introduces different ways of "formalizing" linguistic analyses, with
examples from natural language syntax. Students learn to identify recurrent themes in
generative grammar, seeing how alternative conceptualizations lead to different analytical
trade-offs. Using distinctions such as rule vs constraint, transformational vs. monostratal
and violable vs. inviolable, students emerge better able to assess others' work in a variety
of formalisms, and better able to deploy formalism in their own analyses.
LING 4485/6485: Topics in Computational Linguistics
Current topics in computational linguistics. Recent topics include computational models
for Optimality Theory and finite state models.
LING 2264: Language, Mind, and Brain
An introduction to neurolinguistics, this course surveys topics such as aphasia,
hemispheric lateralization and speech comprehension as they are studied via neuroimaging,
intracranial recording and other methods. A key focus is the relationship between these data,
linguistic theories, and more general conceptions of the mind.