A Unified Neural Coherence Model

Han Cheol Moon, Tasnim Mohiuddin, Shafiq Joty, Chi Xu


Abstract
Recently, neural approaches to coherence modeling have achieved state-of-the-art results in several evaluation tasks. However, we show that most of these models often fail on harder tasks with more realistic application scenarios. In particular, the existing models underperform on tasks that require the model to be sensitive to local contexts such as candidate ranking in conversational dialogue and in machine translation. In this paper, we propose a unified coherence model that incorporates sentence grammar, inter-sentence coherence relations, and global coherence patterns into a common neural framework. With extensive experiments on local and global discrimination tasks, we demonstrate that our proposed model outperforms existing models by a good margin, and establish a new state-of-the-art.
Anthology ID:
D19-1231
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2262–2272
Language:
URL:
https://aclanthology.org/D19-1231
DOI:
10.18653/v1/D19-1231
Bibkey:
Cite (ACL):
Han Cheol Moon, Tasnim Mohiuddin, Shafiq Joty, and Chi Xu. 2019. A Unified Neural Coherence Model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2262–2272, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
A Unified Neural Coherence Model (Moon et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1231.pdf