YNU - HPCC at WASSA 2023: Using Text-Mixed Data Augmentation for Emotion Classification on Code-Mixed Text Message (2023)

Abstract: Emotion classification on code-mixed texts has been widely used in real-world applications. In this paper, we build a system that participates in the WASSA 2023 Shared Task 2 for emotion classification on code-mixed text messages from Roman Urdu and English. The main goal of the proposed method is to adopt a text-mixed data augmentation for robust code-mixed text representation. We mix texts with both multi-label (track 1) and multi-class (track 2) annotations in a unified multilingual pre-trained model, i.e., XLM-RoBERTa, for both subtasks. Our results show that the proposed text-mixed method performs competitively, ranking first in both tracks, achieving an average Macro F1 score of 0.9782 on the multi-label track and of 0.9329 on the multi-class track.

## introduction emotion classification is a fundamental task in natural language processing (nlp). the main purpose is to identify the emotions in a written text that potentially represents the author's mental state. compared with single-label emotion classification, multi-label emotion classification is more difficult to determine all possible emotions instead of only one emotion in a given text. accordingly, multilabel classification has shown wide applications, such as health care and e-learning @xcite . with the rapid growth of the internet, linguistic code-mixed culture has become one of the most prominent communication approaches. codemixed texts represent texts written by two or more languages, simultaneously. according to @xcite , more than half of europeans use codemixed texts in communication. thus, providing a more accurate judgment about the potential emotional state of such code-mixed texts is essential for various real-world applications, such as author profiling and sentiment analysis @xcite . ## related work emotion classification is a challenging nlp task that aims to automatically classify text documents into one or more predefined emotion categories. it has long been of interest to researchers in areas such as sentiment analysis, opinion mining, and social media analysis. recent studies have explored the use of different deep learning architectures, such as convolutional neural networks (cnns) @xcite and recurrent neural networks (rnns) @xcite , to improve the performance of emo-tion classification. other studies have focused on the development of pretraining models such as bert @xcite , albert (a lite bert) @xcite and roberta @xcite , which have been shown to achieve stateof-the-art results on various nlp tasks, including emotion classification. a growing body of research has focused on the use of monolingual and cross-lingual models to improve emotion classification. monolingual methods are based on training models on large amounts of data from a single language, while cross-lingual models make use of data from multiple languages to learn more robust representations. cross-lingual models such as xlm @xcite have shown promise in many nlp tasks, including emotion classification. due to the scarcity of low-resource data, data augmentation is essential. mixup @xcite is a simple and effective data augmentation method, which can significantly improve the effect in multiple fields such as image, text, speech, recommendation. different variants of mixing methods have also been designed in the different space, the cutmix @xcite ) method takes a different approach, instead of interpolating two samples from a numerical perspective, but from the spatial perspective of the image, it cuts a random rectangular area on one picture to another picture to generate a new picture. manifold mixup @xcite extends mixup, and extends the mixing of input data (raw input data) to the output mixing of the intermediate hidden layer. ## experimental results in this section, extensive experiments were conducted for both mcec and mlec tracks. ## conclusions in this paper, we proposed described our system submission wassa 2023 shared task 2 in emotion classification. our system utilizes the text-mixed method and cross-lingual plms for robust representation of code-mixed texts. as a result, our system won the first rank in both tracks. in the future, we will explore the text-mixed method applied to large amounts of unlabeled code-mixed texts for better performance.