The usage of more than one language in the same text is referred to as Code Mixed. It is evident that there is a growing degree of adaption of the use of code-mixed data, especially English with a regional language, on social media platforms. Existing deep-learning models do not take advantage of the implicit language information in the code-mixed text. Our study aims to improve BERT-based models performance on low-resource Code-Mixed Hindi-English Datasets by experimenting with language augmentation approaches. We propose a pipeline to improve code-mixed systems that comprise data preprocessing, word-level language identification, language augmentation, and model training on downstream tasks like sentiment analysis. For language augmentation in BERT models, we explore word-level interleaving and post-sentence placement of language information. We have examined the performance of vanilla BERT-based models and their code-mixed HingBERT counterparts on respective benchmark datasets, comparing their results with and without using word-level language information. The models were evaluated using metrics such as accuracy, precision, recall, and F1 score. Our findings show that the proposed language augmentation approaches work well across different BERT models. We demonstrate the importance of augmenting code-mixed text with language information on five different code-mixed Hindi-English downstream datasets based on sentiment analysis, hate speech detection, and emotion detection.

翻译：在同一文本中使用多种语言的现象称为混合语码。显然，在社交媒体平台上，混合语码数据（尤其是英语与地方语言混用）的使用正在日益普及。现有深度学习模型未能充分利用混合语码文本中的隐含语言信息。本研究旨在通过实验语言增强方法，提升基于BERT的模型在低资源印地语-英语混合语码数据集上的性能。我们提出一个改进混合语码系统的流水线，包含数据预处理、词级语言识别、语言增强以及下游任务（如情感分析）中的模型训练。针对BERT模型的语言增强，我们探索了词级交错与句后语言信息嵌入两种方法。我们考察了原始BERT模型及其混合语码对应模型HingBERT在各自基准数据集上的表现，并对比了使用与不使用词级语言信息时的结果。模型通过准确率、精确率、召回率和F1分数等指标进行评估。研究发现，所提出的语言增强方法在不同BERT模型上均表现良好。我们基于情感分析、仇恨言论检测和情绪检测等五个不同的印地语-英语混合语码下游数据集，论证了为混合语码文本添加语言信息的重要性。