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Answerer in questioner's mind: Information theoretic approach to goal-oriented visual dialog

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Issue Date
2018-01
Publisher
Neural information processing systems foundation
Citation
Advances in Neural Information Processing Systems, Vol.2018-December, pp.2579-2589
Abstract
© 2018 Curran Associates Inc.All rights reserved.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.
ISSN
1049-5258
URI
https://hdl.handle.net/10371/179344
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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