Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network-Reference-Cited by-同舟云学术

Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network

Published:2021 Issue:2 Volume:28 Page:321-349
ISSN:1340-7619
Container-title:Journal of Natural Language Processing
language:en
Short-container-title:Journal of Natural Language Processing

Author:

Kamigaito Hidetaka¹,Hayashi Katsuhiko²,Hirao Tsutomu³,Masaaki Nagata ³,Okumura Manabu⁴

Affiliation:

1. NTT Communication Science Laboratories, NTT Corporation. Currently, he is working in Institute of Innovative Research, Tokyo Institute of Technology.

2. NTT Communication Science Laboratories, NTT Corporation. Currently, he is working at Gunma University.

3. NTT Communication Science Laboratories, NTT Corporation

4. Institute of Innovative Research, Tokyo Institute of Technology

Publisher

Association for Natural Language Processing

Link

https://www.jstage.jst.go.jp/article/jnlp/28/2/28_321/_pdf

Reference38 articles.

1. Aharoni, R. and Goldberg, Y. (2017). “Towards String-To-Tree Neural Machine Translation.” In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 132–140, Vancouver, Canada. Association for Computational Linguistics.

2. Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., and Simaan, K. (2017). “Graph Convolutional Encoders for Syntax-aware Neural Machine Translation.” In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1957–1967, Copenhagen, Denmark. Association for Computational Linguistics.

3. Berg-Kirkpatrick, T., Gillick, D., and Klein, D. (2011). “Jointly Learning to Extract and Compress.” In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 481–490, Portland, Oregon, USA. Association for Computational Linguistics.

4. Chen, Y. and Pan, R. (2017). “Automatic Emphatic Information Extraction from Aligned Acoustic Data and Its Application on Sentence Compression.” In Proceedings of the 31st AAAI Conference on Artificial Intelligence, February 4–9, 2017, San Francisco, California, USA., pp. 3422–3428.

5. Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). “On the Properties of Neural Machine Translation: Encoder–Decoder Approaches.” In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111, Doha, Qatar. Association for Computational Linguistics.

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring the Impact of Syntactic Structure Information on Unknown Entity Recognition in Transformer-based Natural Language Understanding;2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI);2023-07-08

2. An Explanation of “<i>Effectiveness of Syntactic Dependency Information for Higher-Order Syntactic Attention Network</i>”;Journal of Natural Language Processing;2022

3. Unified Interpretation of Softmax Cross Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding;Journal of Natural Language Processing;2021