Pragmatic Models for Generating and Following Grounded Instructions

Friday, September 14, 2018, 3:00 pm - 4:00 pm PDTiCal
11th Floor Large Conference Room [1135]
This event is open to the public.
NL Seminar
Daniel Fried (UC Berkeley)
Video Recording:

Abstract: To generate language, we model what to say, why not also model how listeners will react? We show how pragmatic inference can be used to both generate and interpret natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about how listeners will react upon hearing instructions, and reason counterfactually about why speakers produced the instructions they did. We find that this inference procedure improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at generating instructions correctly interpreted by humans) in diverse settings, including navigating through real-world indoor environments.

Bio: Daniel Fried is a PhD student at UC Berkeley, working with Dan Klein on grounded semantics and structured prediction in natural language processing. Previously, he received a BS from the University of Arizona and an MPhil from the University of Cambridge. His work has been supported by a Churchill Scholarship, NDSEG Fellowship, Huawei / Berkeley AI Fellowship, and Tencent Fellowship.

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