Abstract
Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argument-relation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.- Anthology ID:
- 2020.acl-main.14
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 139–148
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.14
- DOI:
- 10.18653/v1/2020.acl-main.14
- Cite (ACL):
- Ruifang He, Jian Wang, Fengyu Guo, and Yugui Han. 2020. TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 139–148, Online. Association for Computational Linguistics.
- Cite (Informal):
- TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (He et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.acl-main.14.pdf