  Code switching is a linguistic phenomenon that 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. Furthermore, the adversarial training with a multi-lingual model
is used to achieve 1st place of SemEval-2020 Task 9 Hindi-English sentiment
classification competition.
