Jesse Thomason

University of Texas at Austin Ph.D. Candidate

NSF Graduate Research Fellow

I work at the intersection of natural language processing and robotics. My research interests are primarily in semantic understanding and language grounding in human-robot dialogs. I focus on algorithms that bootstrap robot understanding from interaction with humans, improving language understanding and perceptual grounding for whatever task and domain an embodied robot operates in.

Curriculum Vitae

Google Scholar Profile

Selected Publications

Opportunistic Active Learning for Grounding Natural Language Descriptions
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Justin Hart, Peter Stone, and Raymond J. Mooney.
Proceedings of the 1st Annual Conference on Robot Learning (CoRL-17), (to appear), Mountain View, California, November 2017.
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Multi-Modal Word Synset Induction
Jesse Thomason and Raymond J. Mooney.
Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pages 4116-4122, Melbourne, Australia, August 2017.
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Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy"
Jesse Thomason, Jivko Sinapov, Maxwell Svetlik, Peter Stone, and Raymond Mooney.
Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pages 3477-3483, New York, July 2016.
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Learning to Interpret Natural Language Commands through Human-Robot Dialog
Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone.
Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI-15), pages 1923-1929, Buenos Aires, Argentina, July 2015.
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Other Conference and Journal Papers

Improving Black-box Speech Recognition using Semantic Parsing
Rodolfo Corona, Jesse Thomason, and Raymond Mooney.
Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), (to appear), Taipei, Taiwan, November 2017.
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Integrated Learning of Dialog Strategies and Semantic Parsing
Aishwarya Padmakumar, Jesse Thomason, Raymond J. Mooney.
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pages 547-557, Valencia, Spain, April 2017.
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BWIBots: A platform for bridging the gap between AI and human--robot interaction research
Piyush Khandelwal, Shiqi Zhang, Jivko Sinapov, Matteo Leonetti, Jesse Thomason, Fangkai Yang, Ilaria Gori, Maxwell Svetlik, Priyanka Khante, Vladimir Lifschitz, J. K. Aggarwal, Raymond Mooney, and Peter Stone.
International Journal of Robotics Research (IJRR), February 2017.
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Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild
Jesse Thomason, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, and Raymond Mooney.
Proceedings of the 25th International Conference on Computational Linguistics (COLING), pages 1218-1227, Dublin, Ireland, August 2014.
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Prosodic Entrainment and Tutoring Dialogue Success
Jesse Thomason, Huy Nguyen, and Diane Litman.
Proceedings of the 16th International Conference on Artificial Intelligence in Education (AIED), pages 750-753, Memphis, TN, July 2013.
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Differences in User Responses to a Wizard-of-Oz versus Automated System
Jesse Thomason and Diane Litman.
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 796-801, Atlanta, GA, June 2013.
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Exploring Multi-dimensional Data on Mobile Devices with Single Hand Motion and Orientation Gestures
Jesse Thomason and Jingtao Wang.
Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services companion (MobileHCI), pages 173-176, San Francisco, CA, September 2012.
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Differentiated service strategies for ad-hoc wireless sensor networks in harsh communication environments
Jesse D. Thomason, Kenji Yoshigoe, R. B. Lenin, James M. Bridges, and Srini Ramaswamy.
Springer: Wireless Networks Volume 18, Number 5 (2012), 551-564, DOI: 10.1007/s11276-012-0418-3.
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Workshops, Invited Talks, etc.

Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
Wesley Tansey, Jesse Thomason, and James G. Scott.
Proceedings of the ICML Workshop on Human Interpretability in Machine Learning (WHI 2017), pages 44-48, Sydney, Australia, August 2017.
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Guiding Interaction Behaviors for Multi-modal Grounded Language Learning
Jesse Thomason, Jivko Sinapov, and Raymond J. Mooney.
Proceedings of the First Workshop on Language Grounding for Robotics (RoboNLP), pages 20-24, Vancouver, Canada, August 2017.
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Continuously Improving Robotic Natural Language Understanding with Semantic Parsing, Dialog, and Multi-modal Percpetion
Jesse Thomason.
Humanity Centered Robotics Initiative (HCRI), Brown University, Providence, Rhode Island, May 2017.
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Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception
Jesse Thomason.
Doctoral Dissertation Proposal, December 2016.
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