To generate language, natural language processing systems predict what to say---why not also predict how listeners will respond? We show how language generation and interpretation across varied grounded domains can be improved through pragmatic inference: explicitly reasoning about the actions and intents of the people that the systems interact with. We train neural generation and interpretation models which ground language into a world context, then layer a pragmatic inference procedure on top of these models. This pragmatic procedure predicts how human listeners will interpret text generated by the models, and reasons counterfactually about why human speakers produced the text they did. We find this approach improves models' success at generating and interpreting instructions in real indoor environments, as well as in a challenging spatial reference dialogue task.
Daniel Fried is a final-year computer science PhD student at UC Berkeley, advised by Dan Klein. His research in NLP focuses on language grounding: tying language to world contexts, for tasks like visual- and embodied-instruction following, text generation, and dialogue. Previously, he graduated with a BS from the University of Arizona and an MPhil from the University of Cambridge. His work has been supported by a Google PhD Fellowship, an NDSEG Fellowship, and a Churchill Scholarship.
Faculty Host: Eric Nyberg
Language Technologies Institute
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