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        <title>Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification</title>
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    <name type="personal">
        <namePart type="given">Jiaxiang</namePart>
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    <name type="personal">
        <namePart type="given">Xuan</namePart>
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    <name type="personal">
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    <abstract>Code switching is a linguistic phenomenon which may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomenon is more and more popular. However, there are little research and data in this area, especially in code-mixing sentiment classification. In this work, the domain transfer learning from state-of-the-art uni-language model ERNIE is tested on the code-mixing dataset, and surprisingly, a strong baseline is achieved. And further more, the adversarial training with a multi-lingual model is used to achieved 1st place of SemEval-2020 Task9 Hindi-English sentiment classification competition.</abstract>
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%0 Conference Proceedings
%T Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
%A Liu, Jiaxiang
%A Chen, Xuyi
%A Feng, Shikun
%A Wang, Shuohuan
%A Ouyang, Xuan
%A Sun, Yu
%A Huang, Zhengjie
%A Su, Weiyue
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F liu-etal-2020-kk2018
%X Code switching is a linguistic phenomenon which may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomenon is more and more popular. However, there are little research and data in this area, especially in code-mixing sentiment classification. In this work, the domain transfer learning from state-of-the-art uni-language model ERNIE is tested on the code-mixing dataset, and surprisingly, a strong baseline is achieved. And further more, the adversarial training with a multi-lingual model is used to achieved 1st place of SemEval-2020 Task9 Hindi-English sentiment classification competition.
%R 10.18653/v1/2020.semeval-1.103
%U https://aclanthology.org/2020.semeval-1.103
%U https://doi.org/10.18653/v1/2020.semeval-1.103
%P 817-823

Markdown (Informal)

[Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification](https://aclanthology.org/2020.semeval-1.103) (Liu et al., SemEval 2020)

• Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification (Liu et al., SemEval 2020)

ACL

• Jiaxiang Liu, Xuyi Chen, Shikun Feng, Shuohuan Wang, Xuan Ouyang, Yu Sun, Zhengjie Huang, and Weiyue Su. 2020. Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 817–823, Barcelona (online). International Committee for Computational Linguistics.