Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in an issue-specific manner. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog system modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance, and (3) integrating domain ontology knowledge into the dialog model in both pipeline and end-to-end models. We also review the recent progresses in dialog evaluation and some widely-used corpora. We believe that this survey can shed a light on future research in task-oriented dialog systems.
翻译:由于在人-计算机互动和自然语言处理方面的重要性和价值,面向任务的对话系统正在学术界和工业界引起越来越多的注意。在本文件中,我们以针对具体问题的方式调查最近的进展和挑战。我们讨论了面向任务的对话系统的三个关键议题:(1) 提高数据效率,以便利在低资源环境下建立对话系统模型;(2) 模拟对话政策学习的多方向动态,以取得更好的任务完成业绩;(3) 在编审模式和终端至终端模式中,将域内学知识纳入对话模式。我们还审查了最近在对话评价和一些广泛使用的公司方面取得的进展。我们认为,这项调查可以说明今后在面向任务的对话系统中进行的研究。