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

arXiv: https://arxiv.org/abs/2009.03673 · HTML: https://mlsanity.com/paper/2009.03673/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [AdvCodeMix: Adversarial Attack on Code-Mixed Data](https://mlsanity.com/paper/2111.00350/) (2021)
- [PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages](https://mlsanity.com/paper/2111.06599/) (2021)
- [Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns](https://mlsanity.com/paper/2211.07628/) (2022)
- [UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection](https://mlsanity.com/paper/2104.08635/) (2021)
- [Improving Cross-lingual Representation for Semantic Retrieval with Code-switching](https://mlsanity.com/paper/2403.01364/) (2024)
- [Share What You Already Know: Cross-Language-Script Transfer and Alignment for Sentiment Detection in Code-Mixed Data](https://mlsanity.com/paper/2402.04542/) (2024)

## Abstract (arXiv)

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.