Abstract
We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.- Anthology ID:
- P17-1121
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1320–1330
- Language:
- URL:
- https://aclanthology.org/P17-1121
- DOI:
- 10.18653/v1/P17-1121
- Cite (ACL):
- Dat Tien Nguyen and Shafiq Joty. 2017. A Neural Local Coherence Model. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1320–1330, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Neural Local Coherence Model (Tien Nguyen & Joty, ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1121.pdf
- Code
- datienguyen/cnn_coherence