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Code switching is a linguistic phenomenon that may occur within amultilingual setting where speakers share more than one language. With theincreasing communication between groups with different languages, thisphenomenon is more and more popular. However, there are little research anddata in this area, especially in code-mixing sentiment classification. In thiswork, the domain transfer learning from state-of-the-art uni-language modelERNIE is tested on the code-mixing dataset, and surprisingly, a strong baselineis achieved. Furthermore, the adversarial training with a multi-lingual modelis used to achieve 1st place of SemEval-2020 Task 9 Hindi-English sentimentclassification competition.

kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification