Comparative F1-score Analysis of Multilingual and Monolingual Transformers on Adversarially Perturbed Code-Mixed Hate Speech

Detecting and classifying instances of hate in social media text has been a problem of interest in Natural Language Processing in the recent years. Our work leverages state of the art Transformer language models to identify hate speech in a multilingual setting. Capturing the intent of a post or a comment on social media involves careful evaluation of the language style, semantic content and additional pointers such as hashtags and emojis. In this paper, we look at the problem of identifying whether a Twitter post is hateful and offensive or not. We further discriminate the detected toxic cont

Research goal: How does the F1-score of multilingual transformer models compare to monolingual models when evaluated on code-mixed hate speech datasets with adversarial perturbations?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.