Goal-oriented dialogue has been paid attention for its numerous applications in artificial intelligence. To solve this task, deep learning and reinforcement learning have recently been 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 algorithm for goal-oriented dialogue. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intent 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 dialogue tasks: "MNIST Counting Dialog" and "GuessWhat?!." In our experiments, AQM outperforms comparative algorithms and makes human-like dialogue. We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems.

本研究提出了一种新的信息理论策略： 基于该策略的 AQM 模型能有效地处理面向目标的对话任务， 尤其是在问答者句子序列学习方面的复杂性，与其他相比，经验证明AQm更胜一筹。

问者心中的回答者：面向目标的视觉对话的信息论方法