Recent Advances on Human-Computer Dialogue

摘要

Human-Computer dialogue systems provide a natural language based interface between human and computers. They are widely demanded in network information services, intelligent accompanying robots, and so on. A Human-Computer dialogue system typically consists of three parts, namely Natural Language Understanding (NLU), Dialogue Management (DM) and Natural Language Generation (NLG). Each part has several different subtasks. Each subtask has been received lots of attentions, many improvements have been achieved on each subtask, respectively. But systems built in traditional pipeline way, where different subtasks are assembled sequently, suffered from some problems such as error accumulation and expanding, domain transferring. Therefore, researches on jointly modeling several subtasks in one part or cross different parts have been prompted greatly in recent years, especially the rapid developments on deep neural networks based joint models. There is even a few work aiming to integrate all subtasks of a dialogue system in a single model, namely end-to-end models. This paper introduces two basic frames of current dialogue systems and gives a brief survey on recent advances on variety subtasks at first, and then focuses on joint models for multiple subtasks of dialogues. We review several different joint models including integration of several subtasks inside NLU or NLG, jointly modeling cross NLG and DM, and jointly modeling through NLU, DM and NLG. Both advantages and problems of those joint models are discussed. We consider that the joint models, or end-to-end models, will be one important trend for developing Human-Computer dialogue systems.