Computation, Language, and Meaning
Band of Researchers

What do all of the languages of the world have in common? Why and how did these linguistic universals arise? What do they tell us about human cognition and how can we harness them to build better language technologies?

At CLMBR, we address these and related questions from a computational and experimental perspective. We are a lab hosted at the Linguistics Department of the University of Washington, as part of the larger Treehouse computational linguistics lab.

News

Congratulations to C.M. Downey, who will be starting as an Assistant Professor of Linguistics and Data Science at the University of Rochester this Fall! [February 2024]

Congratulations to the brilliant Dr. Naomi Tachikawa Shapiro for successfully defending her dissertation (the first in the group)! Naomi is now off to a postdoc in computational psycholinguistics at Nijmegen! [Dec 2023]

Five papers accepted at various EMNLP Workshops; congratulations to everyone involved! [Oct 2023]

Upcoming invited talks:

  • Workshop on internal and external pressures shaping language @ ESSLLI 2023
  • Ohio State Linguistics Colloquium (Sept 2023)
  • Workshop on computational approaches to language typology and evolution (Oct 2023)
  • UMass Amherst Linguistics Colloquium (Dec 2023)
  • University of Edinburgh Center for Langauge Evolution (Winter 2024)

Congratulations to everyone who graduated in Spring 2023! CLMSer Yifan Jiang is off to a PhD in CS at Waterloo! Our first batch of undergrads has also graduated: Minghe Zhang is off to an MS in Stats at STanford and we are lucky to have Leroy Wang and Gladys Wang staying on board for the CLMS program. Congratulations everyone!

Several new papers and preprints posted [February 2023]

Upcoming invited talks:

  • McDonnell Foundation workshop on monotonicity @ Brown (Dec 2022)
  • UC Irvine Language Science Colloquium (Feb 2023)
  • MIT Breakstone Speaker Series on Language, Mind and Computation (March 2023)
  • UC Davis Linguistics Colloquium (May 2023)

Two papers accepted at BlackboxNLP! [October 2022]

Emergent Communication Fine-tuning (EC-FT) for Pretrained Language Models gets runner-up best paper at the Emergent Communication Workshop @ ICLR! [April 2022]

Invited talks at Ettinger lab @ U Chicago and CLASP @ Gothenburg [March 2022]

Two ACL papers and a SALT paper accepted! [February 2022]

Added several publications and preprints from the second half of 2021. [January 2022]

Two new papers! [May 2021]

Three new preprints posted! See the Publications section below for more info and links. (April 2021)

"Referential and General Calls in Primate Semantics" published in Linguistics and Philosophy

Invited talks at TedLab@MIT (May 2021) and Montana State Computer Science Colloquium (Feb 2021)

Two papers and an extended abstract accepted at BlackboxNLP! (September 2020)

Invited talks at Human Interactivity and Language Lab (June 2020) and the Computation and Language Lab (October 2020)

Shane is co-organizing a workshop on Computational and Experimental Explanations in Semantics and Pragmatics, co-located with ESSLLI (August 2020 2021)

Daniel is co-organizing a track on deep learning in search at NIST's TREC 2020 (Nov 2020)

"On the Spontaneous Emergence of Discrete and Compositional Singals" (with Nur Lan and Emmanuel Chemla) accepted at ACL; preprint available soon (April 2020)

"Complexity/informativeness trade-off in the domain of indefinite pronouns" presented at Semantics and Linguistic Theory (SALT 30) (April August 2020)

Semantic Expressivism for Epistemic Modals appears online at Linguistics and Philosophy (March 2020)

Invited talk at Language Change: Theoretical and Experimental Perspectives at the Hebrew Univeristy of Jerusalem (March 2020 January 2021)

Most, but not more than half is proportion-dependent and sensitive to individual differences appears in the proceedings of Sinn und Bedeutung (SuB 24) (February 2020)

Daniel is teaching Deep Learning in Search at ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (January 2020)

"Quantifiers in natural language optimize the simplicity/informativeness trade-off" presented at the 22nd Amsterdam Colloquium (December 2019)

"An Explanation of the Veridical Uniformity Universal" appears online at Journal of Semantics (open access) (December 2019)

"Ease of Learning Explains Semantic Universals" appears online at Cognition (November 2019)

"Learnability and Semantic Universals" appears online at Semantics & Pragmatics (November 2019)

"Towards the Emergence of Non-trivial Compositionality" accepted at Philosophy of Science (September 2019)

Shane presents two papers—"The evolution of monotone quantifiers via iterated learning" and Complexity and learnability in the explanation of semantic universals"—at CogSci (July 2019)

Lewis presents Neural Models of the Psychosemantics of "most" at Cognitive Modeling and Computational Linguistics (CMCL) (June 2019)

Research

Semantic Universals

A major line of recent work has involved explaining semantic universals: shared properties of meaning across all natural languages. We have been using machine learning to argue that the languages of the world express meanings that are easier to learn. Ongoing work extends this approach to more linguistic domains, compares it to other candidate explanations, and integrates learnability with models of language change/evolution.

Representative papers:

Emergent Communication

By placing artificial agents in simulated environments, we can use reinforcement learning to study the emergence of fundamental features of human language. A particular focus has been on non-trivial compositionality: rudimentary forms of hierarchical structure that exhibit one linguistic item modifying another, as in non-intersective adjectives. This research line has both theoretical and practical interest, since autonomous agents such as vehicles may benefit from learning their own communication system.

Representative papers:

Cognitive Science and NLP

We also conduct studies investigating the cognitive underpinnings of language understanding. How do speakers represent meanings internally and how do these representations interface with other cognitive modules?

Moreover, as natural language processing tools become increasingly more powerful, we believe that two-way interaction between cognitive science and NLP will be increasingly important. On the one hand: insights from human language understanding can help us analyze, understand, and build better machines. On the other hand: increasing sophistication in modeling from NLP can provide inspiration and insights in modeling human behavior. Our lab has ongoing projects in both directions.

Representative papers:

People

Principal Investigator

Shane Steinert-Threlkeld

Assistant Professor, Linguistics
Personal Website
shanest AT uw DOT edu

Shane is an Assistant Professor in Linguistics, where he directs the CLMBR group and teaches in the CLMS program. When not researching or teaching, he spends as much time as possible climbing rocks.

Graduate Students

C.M. Downey

PhD Student in Computational Linguistics
Personal Website
cmdowney AT uw DOT edu

C.M. Downey works to develop methodologies to improve Natural Language Processing in low-resource languages and settings, specializing in unsupervised/self-supervised methods, multilingual language modeling, and cross-lingual transfer. Outside of work, Downey enjoys backpacking, bread baking, refining spotify playlists, and (badly) playing the piano.

Chris Haberland

PhD Student in Computational Linguistics
Personal Website
haberc_ATT uw_DOTT [educ. abbrev.]

Chris studies multimodal language models and natural languages through controlled computational experiments. He is interested in advancing reinforcement learning techniques to emulate language evolution, acquisition, and processing. Chris is also working to build NLP resources for Italic and Mexican languages.

Cassie Maz

PhD Student in Computational Linguistics
Personal Website
cassam5 AT uw DOT edu

Cassie's main research interest is the demand for and development of sign langauge translation tools. Outside of research, she enjoys watching TV and the Sisyphean task of finishing books on her evergrowing reading list.

Amélie Thu Tâm Reymond

Master's Student in Computational Linguistics
attr AT uw DOT edu

Amélie is interested in computational semantics and is working on her master's thesis on compositionality and multilingual language models. In her free time she loves to dance and read (anything from literary theory to true crime).

Naomi Tachikawa Shapiro

PhD Student in Computational Linguistics
Personal Website
tsnaomi AT uw DOT edu

Naomi studies how humans and machines process language, drawing on methodologies from psycholinguistics and machine learning. Aside from research, she loves experimenting with art and design, playing piano, and watching too much TV.

Leroy Wang

Master's Student in Computational Linguistics

Alumni

Publications

Preprints

An Efficient Communication Analysis of Modal Typology
Nathaniel Imel, Qingxia Guo, Shane Steinert-Threlkeld
preprint

The Weighted Möbius Score: A Unified Framework for Feature Attribution
Yifan Jiang, Shane Steinert-Threlkeld
preprint

Deontic priority in the lexicalization of impossibility modals
Wataru Uegaki, Anne Mucha, Nathaniel Imel, Shane Steinert-Threlkeld
preprint

Anti-Babel: Three Degrees of Interspecies Comprehension
Philippe Schlenker, Camille Coye, Ambre Salis, Shane Steinert-Threlkeld, Lucie Ravaux, Emmanuel Chemla
preprint

Learning Compositional Negation in Populations of Roth-Erev and Neural Agents
Graham Todd, Shane Steinert-Threlkeld, Christopher Potts
preprint

2024

Minimal Compositionality versus Bird Implicatures: Two Theories of ABC-D Sequences in Japanese Tits
Philippe Schlenker, Ambre Salis, Maël Leroux, Camille Coye, Luigi Rizzi, Shane Steinert-Threlkeld, Emmanuel Chemla
Biological Reviews
preprint

Limitations of a modal analysis of before and after
Toshiyuki Ogihara, Shane Steinert-Threlkeld
Semantics & Pragmatics
official

2023

Learning to translate by learning to communicate
C.M. Downey, Leo Z. Liu, Xuhui Zhou, Shane Steinert-Threlkeld
Multilingual Representation Learning (MRL 2023)
official preprint code

Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages
C.M. Downey, Terra Blevins, Nora Goldfine, Shane Steinert-Threlkeld
Multilingual Representation Learning (MRL 2023), best paper award
official preprint

Evaluating Transformer's Ability to Learn Mildly Context-Sensitive Languages
Shunjie Wang, Shane Steinert-Threlkeld
BlackboxNLP
official preprint

mSCAN: A Dataset for Multilingual Compositional Generalisation Evaluation
Amelie Raymond, Shane Steinert-Threlkeld
GenBench Workshop
official

GQG: Generalized Quantifier Generalization - A Dataset for Evaluating Quantifier Semantics Understanding in Language Models
Leroy Zhifei Wang, Shane Steinert-Threlkeld
GenBench Workshop
official

Iconic Artificial Language Learning: A Conceptual Replication with English Speakers
Naomi Tachikawa Shapiro, Shane Steinert-Threlkeld
CogSci
official preprint

A Semantic Universal for Modality
Shane Steinert-Threlkeld, Nathaniel Imel, Qingxia Guo
Semantics & Pragmatics
official preprint

Quantifiers Satisfying Semantic Universals Have Shorter Minimal Description Length
Iris van de Pol, Paul Lodder, Leendert van Maanen, Shane Steinert-Threlkeld, Jakub Szymanik
Cognition
official code

Uncovering the structure of semantic representations using a computational model of decision-making
Sonia Ramotowska, Shane Steinert-Threlkeld, Leendert Van Maanen, Jakub Szymanik
Cognitive Science
official code + data

2022

Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models
David K. Yi, James V. Bruno, Jiayu Han, Peter Zukerman, Shane Steinert-Threlkeld
BlackboxNLP
official preprint code

Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis
Pangbo Ban, Yifan Jiang, Tianran Liu, Shane Steinert-Threlkeld
BlackboxNLP
official preprint code

Beyond Anthropocentrism in Comparative Cognition: Recentering Animal Linguistics
Philippe Schlenker, Camille Coye, Shane Steinert-Threlkeld, Nathan Klinedinst, Emmanuel Chemla
Cognitive Science
official preprint

Modals semantic universals optimize the simplicity/informativeness trade-off
Nathaniel Imel, Shane Steinert-Threlkeld
SALT
official preprint code

A Database for Modal Semantic Typology
Qingxia Guo, Nathaniel Imel, Shane Steinert-Threlkeld
SIGTYP
official website

A Masked Segmental Language Model for Unsupervised Natural Language Segmentation
C.M. Downey, Fei Xia, Gina-Anne Levow, Shane Steinert-Threlkeld
SIGMORPHON
official preprint code

Indefinite pronouns optimize the simplicity/informativeness trade-off
Milica Denić, Shane Steinert-Threlkeld, Jakub Szymanik
Cognitive Science
official preprint code

Emergent Communication Fine-tuning (EC-FT) for Pretrained Language Models
Shane Steinert-Threlkeld, Xuhui Zhou, Zeyu Liu, C.M. Downey
Emergent Communication Workshop (EmeCom 5) @ ICLR
Runner-up best paper award
official code

Bilingual alignment transfers to multilingual alignment for unsupervised parallel text mining
Chih-chan Tien, Shane Steinert-Threlkeld
ACL
official preprint code

Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages
C.M. Downey, Shannon Drizin, Levon Haroutunian, Shivin Thukral
ACL
official preprint code

Explaining semantic typology, forms and all
Shane Steinert-Threlkeld
Trends in Cognitive Sciences
official

2021

Quantifiers in Natural Language: Efficient Communication and Degrees of Semantic Universals
Shane Steinert-Threlkeld
Entropy
official (open access) code

A multilabel approach to morphosyntactic probing
Naomi Tachikawa Shapiro, Amandalynne Paullada, Shane Steinert-Threlkeld
Findings of EMNLP
official preprint code

Monotone Quantifiers Emerge via Iterated Learning
Fausto Carcassi, Shane Steinert-Threlkeld, Jakub Szymanik
Cognitive Science
official (open access) preprint code

Language Models Use Monotonicity to Assess NPI Licensing
Jaap Jumelet, Milica Denic, Jakub Szymanik, Dieuwke Hupkes, Shane Steinert-Threlkeld
Findings of ACL
official preprint code

Quantifiers satisfying semantic universals are simpler
Iris van de Pol, Paul Lodder, Leendert van Maanen, Shane Steinert-Threlkeld, Jakub Szymanik
CogSci 2021
official preprint code

How social networks affect the repression-dissent puzzle
Shane Steinert-Threlkeld, Zachary Steinert-Threlkeld
PLoS One
official (open access) code

Referential and General Calls in Primate Semantics
Shane Steinert-Threlkeld, Philippe Schlenker, Emmanuel Chemla
Linguistics and Philosophy
official preprint code

2020

Linguistically-Informed Transformations (LIT): A Method for Automatically Generating Contrast Sets
Chuanrong Li, Lin Shengshuo, Zeyu Liu, Xinyi Wu, Xuhui Zhou, and Shane Steinert-Threlkeld, BlackboxNLP
official preprint code

Probing for Multilingual Numerical Understanding in Transformer-Based Language Models
Devin Johnson, Denise Mak, Drew Barker, and Lexi Loessberg-Zahl, BlackboxNLP
official preprint code

Ease of Learning Explains Semantic Universals
Shane Steinert-Threlkeld and Jakub Szymanik, Cognition, vol 195, no. XX, pp. XX.
official preprint code

On the Spontaneous Emergence of Discrete and Compositional Singals
Nur Lan, Emmanuel Chemla, Shane Steinert-Threlkeld, Proceedings of the Association for Computational Linguistics (ACL)
official preprint code

Complexity/informativeness trade-off in the domain of indefinite pronouns
Milica Denic, Shane Steinert-Threlkeld, Jakub Szymanik, Proceedings of Semantics and Linguistic Theory (SALT 30)
official preprint code

Semantic Expressivism for Epistemic Modals
Peter Hawke and Shane Steinert-Threlkeld (alphabetical order), Linguistics and Philosophy, forthcoming.
official (open access) preprint

Most, but not more than half is proportion-dependent and sensitive to individual differences
Sonia Ramotowska, Shane Steinert-Threlkeld, Leendert van Maanen, Jakub Szymanik, Proceedings of Sinn und Bedeutung (SuB 24)
official preprint

Towards the Emergence of Non-trivial Compositionality
Shane Steinert-Threlkeld, Philosophy of Science, forthcoming.
official preprint code

An Explanation of the Veridical Uniformity Universal
Shane Steinert-Threlkeld
Journal of Semantics, vol 37 no 1, pp. 129-144.
official (open access) preprint code

2019

Quantifiers in natural language optimize the simplicity/informativeness trade-off
Shane Steinert-Threlkeld, Proceedings of the 22nd Amsterdam Colloquium, eds. Julian J. Schlöder, Dean McHugh & Floris Roelofsen, pp. 513-522.
official preprint code poster

Learnability and Semantic Universals
Shane Steinert-Threlkeld and Jakub Szymanik, Semantics & Pragmatics, vol 12 issue 4.
early access code

The emergence of monotone quantifiers via iterated learning
Fausto Carcassi, Shane Steinert-Threlkeld (co-first), and Jakub Szymanik, Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019).
preprint code

Complexity and learnability in the explanation of semantic universals
Iris van de Pol, Shane Steinert-Threlkeld, and Jakub Szymanik, Proceedings of the 41st Annual Meeting of the Cognitive Science Society (CogSci 2019).
preprint code

Neural Models of the Psychosemantics of "Most"
Lewis O'Sullivan and Shane Steinert-Threlkeld, Proceedings of the 9th Workshop on Cognitive Modeling and Computational Linguistics (CMCL2019).
official poster code

2018

Paying Attention to Function Words
Shane Steinert-Threlkeld, Emergent Communication Workshop @ 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).
paper poster code

Some of them can Be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Sandro Pezzelle, Shane Steinert-Threlkeld, Raffaella Bernardi, Jakub Szymanik, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018).
official code

Informational Dynamics of Epistemic Possibility Modals
Peter Hawke and Shane Steinert-Threlkeld
Synthese, vol 195 no 10, pp. 4309-4342.
official

2016

Compositional Signaling in a Complex World
Shane Steinert-Threlkeld, Journal of Logic, Language, and Information, vol 25 no 3, pp. 379-397.
official code

Compositionality and Competition in Monkey Alert Calls
Shane Steinert-Threlkeld
Theoretical Linguistics, vol 42 no 1-2, pp. 159-171.
official local

Resources

ULTK: The Unnatural Language Toolkit: this is a Python library (in very active development) to facilitate research using unnatural languages, especially in semantic typology. Current tools focus on efficient communication and grammars for expressions in a language-of-thought.

lm-training: a skeleton for config-driven training of language models on your own data, using HuggingFace. Includes the ability to use HF's Trainer to train recurrent models in addition to transformers.

The Modal Typology Database: a database recording observations about the semantic typology of modals in natural language, focusing on the force-flavor pairs that they can express. This is a growing resource, designed to be easy to contribute to, so please consider so doing!

edugrad: a minimal re-implementation of the PyTorch API for building dynamic computation graphs and computing gradients via backpropagation, designed for pedagogical purposes.