Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, Dong Yu
Abstract
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting ride of the noises is essential to improve its performance. Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words. In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model. Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous state-of-the-art systems.- Anthology ID:
- 2020.emnlp-main.537
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6632–6639
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.537
- DOI:
- 10.18653/v1/2020.emnlp-main.537
- Cite (ACL):
- Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, and Dong Yu. 2020. Semantic Role Labeling Guided Multi-turn Dialogue ReWriter. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6632–6639, Online. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (Xu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.emnlp-main.537.pdf