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Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
Sang-Woo Lee · Yu-Jung Heo · Byoung-Tak Zhang

Tue Dec 04 12:35 PM -- 12:40 PM (PST) @ Room 220 CD
in Tue Track 1 -- Session 2 »

Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer’s intention via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and "GuessWhat?!". In our experiments, AQM outperforms comparative algorithms by a large margin.

Author Information

Sang-Woo Lee (Naver Corp.)
Yu-Jung Heo (Seoul National University)
Byoung-Tak Zhang (Seoul National University & Surromind Robotics)

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