An Explanation of “<i>Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network</i>”
Author:
Affiliation:
1. Tokyo Institute of Technology
Publisher
Association for Natural Language Processing
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.jstage.jst.go.jp/article/jnlp/29/2/29_694/_pdf
Reference14 articles.
1. Hayashi, K., Hirao, T., and Nagata, M. (2016). “Empirical Comparison of Dependency Conversions for RST Discourse Trees.” In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 128–136. Association for Computational Linguistics.
2. Hirao, T., Kamigaito, H., and Nagata, M. (2018). “Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries.” In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4177–4186, Brussels, Belgium. Association for Computational Linguistics.
3. Ishigaki, T., Kamigaito, H., Takamura, H., and Okumura, M. (2019). “Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization.” In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 497–506, Varna, Bulgaria. INCOMA Ltd.
4. Kamigaito, H., Hayashi, K., Hirao, T., and Nagata, M. (2018). “Higher-order Syntactic Attention Network for Longer Sentence Compression.” In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1716–1726.
5. Kamigaito, H., Hayashi, K., Hirao, T., Nagata, M., and Okumura, M. (2021). “Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network.” Journal of Natural Language Processing, 28 (2), pp. 321–349.
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