Abstract
We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model’s ability to selectively focus on the relevant parts of an input sequence.- Anthology ID:
- P17-2040
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 256–262
- Language:
- URL:
- https://aclanthology.org/P17-2040
- DOI:
- 10.18653/v1/P17-2040
- Cite (ACL):
- Samuel Rönnqvist, Niko Schenk, and Christian Chiarcos. 2017. A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 256–262, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations (Rönnqvist et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P17-2040.pdf