Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings

Lianhui Qin, Zhisong Zhang, Hai Zhao


Abstract
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs. However, conventional vector representations usually adopt embeddings at the word level and cannot well handle the rare word problem without carefully considering morphological information at character level. Moreover, embeddings are assigned to individual words independently, which lacks of the crucial contextual information. This paper proposes a neural model utilizing context-aware character-enhanced embeddings to alleviate the drawbacks of the current word level representation. Our experiments show that the enhanced embeddings work well and the proposed model obtains state-of-the-art results.
Anthology ID:
C16-1180
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1914–1924
Language:
URL:
https://aclanthology.org/C16-1180
DOI:
Bibkey:
Cite (ACL):
Lianhui Qin, Zhisong Zhang, and Hai Zhao. 2016. Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1914–1924, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings (Qin et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1180.pdf