My work is dedicated to advancing our foundational understanding of human language. How do we understand what we hear and read? How are we able to convert thoughts into meaningful utterances that others understand? And how do we acquire the knowledge that makes all this possible? My research program sits at the intersection of artificial intelligence, psychology, and linguistics, and tackles these questions through theory, computationally implemented models of language, psychological experimentation, analysis of large linguistic datasets, and more.
My research focuses on linguistic meaning and its interactions with context in language understanding. Most of my work has focused on understanding how different types of linguistic context-dependence affect the way in which listeners exploit contextual information to efficiently approximate the speaker's meaning. To answer these questions, I combine insights from theoretical linguistics and cognitive science more broadly with experimental and computational methods.
My research combines Natural Language Processing (NLP), Computational Linguistics and Cognitive Science. I currently study what eye movements during reading can reveal about the linguistic knowledge and cognitive state of the reader, and how such signal can be used to improve NLP. Other related interests include multilingualism, linguistic typology, treebanking, and grounded language acquisition.
I am interested in how people use language to share their thoughts and feelings. I am particularly fascinated by the context-sensitivity of language understanding, issues of vagueness, and how people learn from linguistic messages. In my research, I use computational models and behavioral experiments and enjoy thinking up novel data analytic methods.
My research focuses on the nature, origins, and utility of abstract linguistic knowledge in children's early development, and how such knowledge grows to support adult language processing. In my research I use a combination of computational models from NLP, corpus studies, web-based experiments, and in-lab experiments. My postdoc is split with the Bergelson Lab at Duke University, where I conduct behavioral experiments with young children (12 - 36 months).
I'm interested in linguistic meaning: how it is acquired by the child, how it is structured in the mind of the speaker, and how it is worked out in the mind of the listener. I study these questions through different computational case studies, combining data and methods from linguistics, psychology, artificial intelligence, and neuroscience. You can find much more about my work on my website, where I also blog about language, cognitive science, and philosophy, among other things.
My research focuses on cognitive models of how language is perceived and acquired, with the goal of connecting these model to social and cultural processes to explain language structure. In particular, I am interested in how properties such as discreteness and compositionality arise in grounded communication systems that evolve over time. I pursue these questions by conducting behavioral experiments that mimic cultural evolutionary processes, and by building probabilistic models of the observed linguistic behavior.
My research develops computational models of how humans resolve ambiguity in language understanding, with the goal of building better systems of artificial intelligence. I am also interested in how brains and machines represent linguistic meaning and structure. I am supported by an NSF Graduate Research Fellowship and the NIH Program in Computationally-Enabled Integrative Neuroscience.
I’m interested in understanding how people process language, focusing in particular on the emergence of meaning from interaction. What speakers mean is often underspecified in what they actually say, and I want to understand how listeners infer the missing pieces of the puzzle. Recently, my main focus in addressing this rather broad question has been on ellipsis, in particular Verb Phrase Ellipsis. In some sense, elliptical utterances represent an extreme form of underspecification, but how the missing information is inferred remains highly controversial. I also work on the topic of inferential language comprehension from two other angles: the rational resolution of multiple implicature-driving forces; and a noisy-channel approach to non-literal interpretation.
I’m interested in the cognitive basis of human language. My current work combines behavioral experiments and computational models to investigate the relevance of linguistic knowledge in learning, reasoning, and judgment.
My research seeks to understand the cognitive underpinning of the production and comprehension of natural language. Speakers often face choices as to how to structure their intended message into an utterance. When multiple options are available, what general principles govern speaker choice? What inferences do comprehenders make about why something was said in a particular way? To answer these questions, I combine analysis of naturalistic language datasets, psycholinguistic experiments, and computational modeling.
I am passionate about building software that can help research in traditionally less computationally intensive fields. My project is about understanding the kinds of contextual cues available to children that helps them resolve ambiguous language. Currently, I am pursuing an MEng degree in EECS.
I'm interested in how language use shapes human interaction and influences our thoughts and beliefs. One of my research projects looks at how the gender information conveyed by pronouns seems to introduce biases between production and comprehension.
In order to understand human intelligence, we need to understand how we can learn a mapping from language to meaning. Particularly, how do we come to associate language descriptions with relations and objects in a grounded environment, such as the real world? How can we use existing knowledge to infer the meanings of descriptions that are not easily exemplified? My approach is to construct computational models that learn this mapping as humans do. You can look at some of my work on my website.
I am a second year PhD student in the department of Linguistics at Harvard University, and a current visiting student at the Computational Psycholinguistics lab. My primary interest is uncovering what neural network language models learn about syntactic structures, and thinking more broadly about how hierarchical structure may be instantiated in distributed systems. I also do work on recursive models of pragmatics, investigating where they succeed and where they break down. Before Grad School I worked as a computational linguist and translator in New York. I did my undergraduate work at Stanford University, in the Symbolic Systems program and the Slavic Literature department.
I am visiting undergraduate student from the university of Osnabruck in Germany. I am interested in the context sensitivity of natural language which allows people to use the same words in order to convey different messages. My project focuses on the syntactic and pragmatic aspects of gradable adjective production and interpretation emerging from the interlocutors' reasoning about each others communicative goals.
I’m a first year undergraduate very much interested in computer science and linguistics and would pursue a degree in probably both disciplines. Right now I’m working with Veronica on a project to analyze how people comprehend language by measuring incremental processing difficulty. Her team devised an experiment tool that uses recurrent neural networks to create “distractor” words to be displayed alongside each word in a sentence. I’m helping with the coding, refactoring, adding new features and will conduct experiments with this tool.
I am interested in how humans produce language in a real time communicative setting. I am currently working with Meilin to investigate the principles that govern a speaker’s choice when given multiple options, which is done by studying Mandarin speakers’ choice of classifier.
I’m interested in understanding how people make decisions during language production. My research project explores the factors affecting speaker choice in a classifier language.
Beining Jenny Zhang
I'm a junior studying computer science. I investigate how eye movements link to language comprehension. In my free time, I enjoy singing and reading fantasy and horror novels!
I'm a first year undergraduate student currently studying computer science. I'm interested in studying the intersection between natural language processing and computer science. My current research project looks at what eye movements can tell us about a reader's comprehension.
My project involves understanding how certain kinds of generic sentences work. These types of sentences are common in everyday speech and provide people with a concise way of conveying a lot of meaning about a category. We are working on implementing a computational model to describe the meaning of these sentences and on testing the predictions of this model experimentally.
Along with MH Tessler, I currently focus on using iterated transmission experiments to study how people use language to share their thoughts. Past research has included probing off-the-shelf word embeddings to see how they can be used to differentiate between semantic relations.
I'm an undergrad studying math with CS (18C), and philosophy (24). My research interests are at the intersection of math, CS, and cognitive science; in particular, I'm interested in understanding how people acquire knowledge, and in how this process is shaped by language. Last year, I worked with MH on a project about vague language modeling, and I'm now working on a project that seeks to understand how humans communicate and prioritize knowledge about learned tasks.
|Klinton Bicknell||Suhas Arehalli||Richard Futrell||Fuyun Wu|
|Rebecca Colavin||K. Michael Brooks||Victoria Fossum||Kasia Hitczenko|
|Gabriel Doyle||Wednesday Bushong||Titus von der Malsburg||Kentaro Nakatani|
|Anubha Kothari||Hannah Campbell||Eva Wittenberg||Yanan Sheng|
|Emily Morgan||Bonnie Chinh||Reuben Cohn-Gordon|
|Bozena Pajak||Abhishek Goyal||Aixiu An|
|Y. Albert Park||Jake Prasad|
|Nathaniel Smith||Agatha Ventura|