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Maxwell Forbes

Research

This is my academic webpage

Bio

I am a fifth-year computer science PhD student studying artificial intelligence at the University of Washington. My advisor is Yejin Choi. My primary field of study is natural language processing.

I am excited about teaching machines to understand language in context. This may be situated in a robotics interaction, grounded in images, or as evidence of our commonsense understanding of the how the world works.

I am a 2016 NSF Graduate Research Fellow. In 2018–2019, I was a student researcher at Google AI, working with Christine Kaeser-Chen and Serge Belongie. I am currently a part-time intern at the Allen Institute for Artificial Intelligence. Contact me at: a picture of my email address

Mentoring

I am grateful to collaborate with some amazing undergraduate students. I’m currently working with:

Previously, I worked with:

I am eternally grateful to the wonderful graduate students who mentored me when I was an undergraduate (and beyond): Mike Chung, Kenton Lee and Yoav Artzi.

Publications

The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi
International Conference on Learning Representations (ICLR), 2020
[demo] [bib]

Neural Naturalist: Generating Fine-Grained Image Comparisons
Maxwell Forbes, Christine Kaeser-Chen, Piyush Sharma, Serge Belongie
Empirical Methods in Natural Language Processing (EMNLP) 2019
[project] [data] [bib]

Do Neural Language Representations Learn Physical Commonsense?
Maxwell Forbes, Ari Holtzman, Yejin Choi
Conference of the Cognitive Science Society (CogSci) 2019
[project] [code] [data] [poster]

Learning to Write with Cooperative Discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, Yejin Choi
Association for Computational Linguistics (ACL) 2018
[code] [demo]

Verb Physics: Relative Physical Knowledge of Actions and Objects
Maxwell Forbes, Yejin Choi
Association for Computational Linguistics (ACL) 2017
[project] [code] [data] [slides]

Robot Programming by Demonstration with Situated Spatial Language Understanding
Maxwell Forbes, Rajesh P. N. Rao, Luke Zettlemoyer, Maya Cakmak
IEEE International Conference on Robotics and Automation (ICRA) 2015
[video] [code]

Robot Programming by Demonstration with Crowdsourced Action Fixes
Maxwell Forbes, Michael Jae-Yoon Chung, Maya Cakmak, Rajesh P. N. Rao
AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 2014
[slides]

Accelerating Imitation Learning through Crowdsourcing
Michael Jae-Yoon Chung, Maxwell Forbes, Maya Cakmak, Rajesh P. N. Rao
IEEE International Conference on Robotics and Automation (ICRA) 2014
[press: Popular Science] [press: IEEE Spectrum]

Peer-reviewed workshops

Programming by Demonstration with Situated Semantic Parsing
Yoav Artzi, Maxwell Forbes, Kenton Lee*, Maya Cakmak
AAAI Fall Symposium Series on Human-Robot Interaction 2014
[slides]

Grounding Antonym Adjective Pairs through Interaction
Maxwell Forbes, Michael Chung, Maya Cakmak, Luke Zettlemoyer, Rajesh P. N. Rao
ACM/IEEE International Conference on Human-Robot Interaction (HRI) Workshop on Asymmetric Interactions 2014
[slides]


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