@inproceedings{niu-etal-2019-enhancing,
title = "Enhancing Local Feature Extraction with Global Representation for Neural Text Classification",
author = "Niu, Guocheng and
Xu, Hengru and
He, Bolei and
Xiao, Xinyan and
Wu, Hua and
Gao, Sheng",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1047",
doi = "10.18653/v1/D19-1047",
pages = "496--506",
abstract = "For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. Meanwhile, two modes are also designed for their interaction. Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations. Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.",
}
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<abstract>For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. Meanwhile, two modes are also designed for their interaction. Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations. Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.</abstract>
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%0 Conference Proceedings
%T Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
%A Niu, Guocheng
%A Xu, Hengru
%A He, Bolei
%A Xiao, Xinyan
%A Wu, Hua
%A Gao, Sheng
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F niu-etal-2019-enhancing
%X For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch. In particular, Encoder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. Meanwhile, two modes are also designed for their interaction. Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations. Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.
%R 10.18653/v1/D19-1047
%U https://aclanthology.org/D19-1047
%U https://doi.org/10.18653/v1/D19-1047
%P 496-506
Markdown (Informal)
[Enhancing Local Feature Extraction with Global Representation for Neural Text Classification](https://aclanthology.org/D19-1047) (Niu et al., EMNLP-IJCNLP 2019)
ACL