Hate detection has long been a challenging task for the NLP community. The
task becomes complex in a code-mixed environment because the models must
understand the context and the hate expressed through language alteration.
Compared to the monolingual setup, we see very less work on code-mixed hate as
large-scale annotated hate corpora are unavailable to make the study. To
overcome this bottleneck, we propose using native language hate samples. We
hypothesise that in the era of multilingual language models (MLMs), hate in
code-mixed settings can be detected by majorly relying on the native language
samples. Even though the NLP literature reports the effectiveness of MLMs on
hate detection in many cross-lingual settings, their extensive evaluation in a
code-mixed scenario is yet to be done. This paper attempts to fill this gap
through rigorous empirical experiments. We considered the Hindi-English
code-mixed setup as a case study as we have the linguistic expertise for the
same. Some of the interesting observations we got are: (i) adding native hate
samples in the code-mixed training set, even in small quantity, improved the
performance of MLMs for code-mixed hate detection, (ii) MLMs trained with
native samples alone observed to be detecting code-mixed hate to a large
extent, (iii) The visualisation of attention scores revealed that, when native
samples were included in training, MLMs could better focus on the hate emitting
words in the code-mixed context, and (iv) finally, when hate is subjective or
sarcastic, naively mixing native samples doesn't help much to detect code-mixed
hate. We will release the data and code repository to reproduce the reported
results.

在这篇论文中，我们通过严格的实证实验填补了多语言语言模型在混合代码环境中对恶意检测的评估不足，并观察到加入少量本地恶意样本以及仅使用本地样本对于提高混合代码恶意检测的效果以及可视化注意力对于定位表达恶意意图的词语在混合代码语境中是有帮助的。