Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning

Ta-Chung Chi, Po-Chun Chen, Shang-Yu Su, Yun-Nung Chen


Abstract
Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasks.
Anthology ID:
I17-2028
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
163–168
Language:
URL:
https://aclanthology.org/I17-2028
DOI:
Bibkey:
Cite (ACL):
Ta-Chung Chi, Po-Chun Chen, Shang-Yu Su, and Yun-Nung Chen. 2017. Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 163–168, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Speaker Role Contextual Modeling for Language Understanding and Dialogue Policy Learning (Chi et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2028.pdf
Code
 MiuLab/Spk-Dialogue