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:
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/I17-2028.pdf
- Code
- MiuLab/Spk-Dialogue