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. © 1963-2018 ACL.
| Original language | English |
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| Title of host publication | COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers |
| Publisher | Association for Computational Linguistics, ACL Anthology |
| Pages | 1914-1924 |
| ISBN (Print) | 9784879747020 |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 26th International Conference on Computational Linguistics, COLING 2016 - Osaka, Japan Duration: 11 Dec 2016 → 16 Dec 2016 |
Publication series
| Name | COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers |
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Conference
| Conference | 26th International Conference on Computational Linguistics, COLING 2016 |
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| Place | Japan |
| City | Osaka |
| Period | 11/12/16 → 16/12/16 |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.Funding
This paper was partially supported by Cai Yuanpei Program (CSC No. 201304490199 and No. 201304490171), National Natural Science Foundation of China (No. 61170114, No. 61672343 and No. 61272248), National Basic Research Program of China (No. 2013CB329401), Major Basic Research Program of Shanghai Science and Technology Committee (No. 15JC1400103), Art and Science Interdisciplinary Funds of Shanghai Jiao Tong University (No. 14JCRZ04), and Key Project of National Society Science Foundation of China (No. 15-ZDA041).