Recent advances and challenges in task-oriented dialog systems

被引：0
|
作者：
Zheng Zhang
Ryuichi Takanobu
Qi Zhu
MinLie Huang
XiaoYan Zhu
机构：
[1] Tsinghua University,Department of Computer Science & Technology
[2] Tsinghua University (THUAI),Institute for Artificial Intelligence
[3] Beijing National Research Center for Information Science & Technology,undefined
来源：
Science China Technological Sciences | 2020年 / 63卷
关键词：
task-oriented dialog systems; natural language understanding; dialog policy; dialog state tracking; natural language generation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：2011 / 2027
页数：16
相关论文
共 50 条
  • [1] Recent advances and challenges in task-oriented dialog systems
    ZHANG Zheng
    TAKANOBU Ryuichi
    ZHU Qi
    HUANG MinLie
    ZHU XiaoYan
    [J]. Science China Technological Sciences, 2020, 63 (10) : 2011 - 2027
  • [2] Recent advances and challenges in task-oriented dialog systems
    ZHANG Zheng
    TAKANOBU Ryuichi
    ZHU Qi
    HUANG MinLie
    ZHU XiaoYan
    [J]. Science China(Technological Sciences), 2020, (10) - 2027
  • [3] Recent advances and challenges in task-oriented dialog systems
    Zhang, Zheng
    Takanobu, Ryuichi
    Zhu, Qi
    Huang, MinLie
    Zhu, XiaoYan
    [J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2020, 63 (10) : 2011 - 2027
  • [4] Novel Feature Discovery for Task-Oriented Dialog Systems
    Ho, Vinh Thinh
    Soliman, Mohamed
    Abujabal, Abdalghani
    [J]. 17TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EACL 2023, 2023, : 782 - 792
  • [5] Incremental Dialog Processing in a Task-Oriented Dialog
    Ghigi, Fabrizio
    Eskenazi, Maxine
    Ines Torres, M.
    Lee, Sungjin
    [J]. 15TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2014), VOLS 1-4, 2014, : 308 - 312
  • [6] Database Search Results Disambiguation for Task-Oriented Dialog Systems
    Qian, Kun
    Kottur, Satwik
    Beirami, Ahmad
    Shayandeh, Shahin
    Crook, Paul
    Geramifard, Alborz
    Yu, Zhou
    Sankar, Chinnadhurai
    [J]. NAACL 2022: THE 2022 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES, 2022, : 1158 - 1173
  • [7] A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning
    Wai-Chung Kwan
    Hong-Ru Wang
    Hui-Min Wang
    Kam-Fai Wong
    [J]. Machine Intelligence Research, 2023, 20 : 318 - 334
  • [8] CONTEXT-AWARE DIALOG RE-RANKING FOR TASK-ORIENTED DIALOG SYSTEMS
    Ohmura, Junki
    Eskenazi, Maxine
    [J]. 2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018), 2018, : 846 - 853
  • [9] A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-oriented Dialogue Policy Learning
    Kwan, Wai-Chung
    Wang, Hong-Ru
    Wang, Hui-Min
    Wong, Kam-Fai
    [J]. MACHINE INTELLIGENCE RESEARCH, 2023, 20 (03) : 318 - 334
  • [10] Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems
    Swamy, Sandesh
    Tabari, Narges
    Chen, Chacha
    Gangadharaiah, Rashmi
    [J]. 17TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, EACL 2023, 2023, : 3102 - 3111
12345