Modeling situations in neural chat bots

Abstract

Social media accumulates vast amounts of online conversations that enable data-driven modeling of chat dialogues. It is, however, still hard to utilize the neural network-based SEQ2SEQ model for dialogue modeling in spite of its acknowledged success in machine translation. The main challenge comes from the high degrees of freedom of outputs (responses). This paper presents neural conversational models that have general mechanisms for handling a variety of situations that affect our responses. Response selection tests on massive dialogue data we have collected from Twitter confirmed the effectiveness of the proposed models with situations derived from utterances, users or time.