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    <abstract>We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is a mask gate on characters, learning the relation among characters in a word. The second is a maxpooling operation on words, adaptively finding the optimal mixture of the atomic and compositional word representations. Finally, the proposed architecture is applied to various sentence composition models, which achieves substantial performance gains over baseline models on sentence similarity task.</abstract>
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%0 Conference Proceedings
%T Exploiting Word Internal Structures for Generic Chinese Sentence Representation
%A Wang, Shaonan
%A Zhang, Jiajun
%A Zong, Chengqing
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-exploiting
%X We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is a mask gate on characters, learning the relation among characters in a word. The second is a maxpooling operation on words, adaptively finding the optimal mixture of the atomic and compositional word representations. Finally, the proposed architecture is applied to various sentence composition models, which achieves substantial performance gains over baseline models on sentence similarity task.
%R 10.18653/v1/D17-1029
%U https://aclanthology.org/D17-1029
%U https://doi.org/10.18653/v1/D17-1029
%P 298-303

Markdown (Informal)

[Exploiting Word Internal Structures for Generic Chinese Sentence Representation](https://aclanthology.org/D17-1029) (Wang et al., EMNLP 2017)

• Exploiting Word Internal Structures for Generic Chinese Sentence Representation (Wang et al., EMNLP 2017)

ACL

• Shaonan Wang, Jiajun Zhang, and Chengqing Zong. 2017. Exploiting Word Internal Structures for Generic Chinese Sentence Representation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 298–303, Copenhagen, Denmark. Association for Computational Linguistics.