Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification

Jiaxiang Liu, Xuyi Chen, Shikun Feng, Shuohuan Wang, Xuan Ouyang, Yu Sun, Zhengjie Huang, Weiyue Su


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.
Anthology ID:
2020.semeval-1.103
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
817–823
Language:
URL:
https://aclanthology.org/2020.semeval-1.103
DOI:
10.18653/v1/2020.semeval-1.103
Bibkey:
Cite (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.
Cite (Informal):
Kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification (Liu et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.103.pdf