Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, Haifeng Wang
Abstract
We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.- Anthology ID:
- D17-1134
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1299–1308
- Language:
- URL:
- https://aclanthology.org/D17-1134
- DOI:
- 10.18653/v1/D17-1134
- Cite (ACL):
- Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, and Haifeng Wang. 2017. Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1299–1308, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification (Lan et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/D17-1134.pdf