Abstract
It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives. We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).- Anthology ID:
- P19-1411
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4201–4207
- Language:
- URL:
- https://aclanthology.org/P19-1411
- DOI:
- 10.18653/v1/P19-1411
- Cite (ACL):
- Linh The Nguyen, Linh Van Ngo, Khoat Than, and Thien Huu Nguyen. 2019. Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4201–4207, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings (Nguyen et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P19-1411.pdf