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