Recent advancements in technology have led to a boost in social media usage which has ultimately led to large amounts of user-generated data which also includes hateful and offensive speech. The language used in social media is often a combination of English and the native language in the region. In India, Hindi is used predominantly and is often code-switched with English, giving rise to the Hinglish (Hindi+English) language. Various approaches have been made in the past to classify the code-mixed Hinglish hate speech using different machine learning and deep learning-based techniques. However, these techniques make use of recurrence on convolution mechanisms which are computationally expensive and have high memory requirements. Past techniques also make use of complex data processing making the existing techniques very complex and non-sustainable to change in data. Proposed work gives a much simpler approach which is not only at par with these complex networks but also exceeds performance with the use of subword tokenization algorithms like BPE and Unigram, along with multi-head attention-based techniques, giving an accuracy of 87.41% and an F1 score of 0.851 on standard datasets. Efficient use of BPE and Unigram algorithms help handle the nonconventional Hinglish vocabulary making the proposed technique simple, efficient and sustainable to use in the real world.

翻译：近期技术进步推动了社交媒体使用的激增，从而产生了大量用户生成数据，其中包含仇恨与攻击性言论。社交媒体使用的语言常为英语与当地母语的混合形式。在印度，印地语被广泛使用，且常与英语发生语码混合，形成了印地语混合语（Hinglish）这一语言现象。已有多种方法采用不同机器学习与深度学习技术对语码混合的印地语混合语仇恨言论进行分类。然而，这些技术依赖循环或卷积机制，计算成本高昂且内存需求较大。既往方法还涉及复杂的数据处理，导致现有技术极为复杂且难以适应数据变化。本研究提出了一种更为简洁的方法，不仅与这些复杂网络性能相当，更通过使用BPE和Unigram等子词分词算法及多头注意力机制提升了表现，在标准数据集上达到了87.41%的准确率和0.851的F1分数。BPE与Unigram算法的高效运用有助于处理非常规的印地语混合语词汇，使所提方法在现实应用中具备简洁性、高效性与可持续性。