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

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

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 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)

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

Daniel Campos

Masters Student in Computational Linguistics
Personal Website
email

Daniel is a Masters student in Computational Linguistics. He is also a Product Manager at Microsoft where he works on metrics for Bing Relevance and the MSMARCO datasets. Outside of work/school, he spends time with his wife and son, makes wine/coffee/kombucha, and tries to ride anything that resembles a board.

Paige Finkelstein

Masters Student in Computational Linguistics
Personal Website
plfink AT uw DOT edu

With a background in literature and software development, Paige is excited to bring her love of languages—both natural and otherwise—to her research as a member of the CLMBR group. She's especially interested in NMT and human-in-the-loop learning.

Devin Johnson

Masters Student in Computational Linguistics
Personal Website
dj1121 AT uw DOT edu

Devin is a master's student with research interests in machine learning, language modeling, and computational semantics. Outside of his studies he especially enjoys learning languages, playing music, cooking, and reading philosophy.

Elena Khasanova

Masters Student in Computational Linguistics
Personal Website
ekhas1 AT uw DOT edu

Elena used to program humans to carry out meaningful conversations in foreign languages, now she switched to computers. She is interested in computational semantics, Natural Language Inference, and NLP-driven educational technology. Outside of her studies, she enjoys bouldering, singing, painting portraits, and learning to sail.

Kalyani Sunil Marathe

Masters Student in Electrical and Computer Engineering
Linkedin Profile
kmarathe AT uw DOT edu

Kalyani is an engineer at heart and a creative problem solver. She is interested in Machine Learning and Natural Language Processing. In her spare time, she enjoys playing piano and quilling paper.

Chih-Chan Tien

Masters Student in Computational Linguistics
cctien AT uw DOT edu

Chih-chan is very intrigued by the computability of the meaning in natural languages. His main interests are formal semantics and computational semantics.

Undergraduate Students

Pengfei He

Applied and Computational Math Sciences

Leroy Wang

Computer Science and Linguistics

Publications

2020

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)
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)

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)
preprint

Leading Conversational Search by Suggesting Useful Questions
Corbin Rosset, Chenyan Xiong, Xia Song, Daniel Campos, Nick Craswell, Saurabh Tiwary and Paul Bennett Proceedings of the 26th Annual Meeting of The Web Conference (WWW).
preprint data

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