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        <title>Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning</title>
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        <namePart type="given">Yanfei</namePart>
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    <name type="personal">
        <namePart type="given">Yangdong</namePart>
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        <namePart type="given">Yuejie</namePart>
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            <title>Proceedings of the 19th Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>Neural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual data, which often suffers from insufficient discriminative knowledge for classification. In this paper, we investigate to improve sentence classification by multilingual data augmentation and consensus learning. Comparing to previous methods, our model can make use of multilingual data generated by machine translation and mine their language-share and language-specific knowledge for better representation and classification. We evaluate our model using English (i.e., source language) and Chinese (i.e., target language) data on several sentence classification tasks. Very positive classification performance can be achieved by our proposed model.</abstract>
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        <date>2020-10</date>
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%0 Conference Proceedings
%T Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning
%A Wang, Yanfei
%A Chen, Yangdong
%A Zhang, Yuejie
%Y Sun, Maosong
%Y Li, Sujian
%Y Zhang, Yue
%Y Liu, Yang
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 October
%I Chinese Information Processing Society of China
%C Haikou, China
%G English
%F wang-etal-2020-improving
%X Neural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual data, which often suffers from insufficient discriminative knowledge for classification. In this paper, we investigate to improve sentence classification by multilingual data augmentation and consensus learning. Comparing to previous methods, our model can make use of multilingual data generated by machine translation and mine their language-share and language-specific knowledge for better representation and classification. We evaluate our model using English (i.e., source language) and Chinese (i.e., target language) data on several sentence classification tasks. Very positive classification performance can be achieved by our proposed model.
%U https://aclanthology.org/2020.ccl-1.78
%P 842-852

Markdown (Informal)

[Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning](https://aclanthology.org/2020.ccl-1.78) (Wang et al., CCL 2020)

• Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning (Wang et al., CCL 2020)

ACL

• Yanfei Wang, Yangdong Chen, and Yuejie Zhang. 2020. Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 842–852, Haikou, China. Chinese Information Processing Society of China.