Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, Satoshi Nakamura
Abstract
We propose a novel method for selecting coherent and diverse responses for a given dialogue context. The proposed method re-ranks response candidates generated from conversational models by using event causality relations between events in a dialogue history and response candidates (e.g., “be stressed out” precedes “relieve stress”). We use distributed event representation based on the Role Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system. Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.- Anthology ID:
- W19-4106
- Volume:
- Proceedings of the First Workshop on NLP for Conversational AI
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Yun-Nung Chen, Tania Bedrax-Weiss, Dilek Hakkani-Tur, Anuj Kumar, Mike Lewis, Thang-Minh Luong, Pei-Hao Su, Tsung-Hsien Wen
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–59
- Language:
- URL:
- https://aclanthology.org/W19-4106
- DOI:
- 10.18653/v1/W19-4106
- Cite (ACL):
- Shohei Tanaka, Koichiro Yoshino, Katsuhito Sudoh, and Satoshi Nakamura. 2019. Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding. In Proceedings of the First Workshop on NLP for Conversational AI, pages 51–59, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding (Tanaka et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/W19-4106.pdf
- Code
- additional community code