论文标题
KK2018在Semeval-2020任务9：用于混合情感分类的对抗培训
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
论文作者
论文摘要
代码切换是一种语言现象，可能会在多语言环境中发生，在该设置中，扬声器共享多种语言。随着与不同语言的群体之间的沟通不断增加，这种现象越来越受欢迎。但是，该领域几乎没有研究和数据，尤其是在代码混合情感分类中。在这项工作中，从最先进的单语言模型Ernie中进行了域转移学习，在混音数据集上测试了测试，令人惊讶的是，实现了强大的基线。此外，使用多语言模型的对抗性训练用于实现Semeval-2020任务的第一名。9印度英语情感分类竞赛。
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.
