论文标题
面向任务的对话系统的最新进展和挑战
Recent Advances and Challenges in Task-oriented Dialog System
论文作者
论文摘要
由于人类计算机互动和自然语言处理的重要性和价值，以任务为导向的对话系统在学术和工业社区中引起了越来越多的关注。在本文中，我们调查了面向任务的对话系统的最新进展和挑战。我们还讨论了针对任务的对话框系统的三个关键主题：（1）提高数据效率以促进低资源设置中的对话框建模，（2）建模对话框策略学习以实现更好的任务完成绩效的多转变动态，（3）将域名本体学知识集成到对话框模型中。此外，我们回顾了对话评估的最新进展和一些广泛使用的语料库。我们认为，尽管这项调查不完整，但可以阐明未来的面向任务对话系统的研究。
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 task-oriented dialog systems. We also discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog 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. Besides, we review the recent progresses in dialog evaluation and some widely-used corpora. We believe that this survey, though incomplete, can shed a light on future research in task-oriented dialog systems.
