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This paper investigates the effectiveness of sentence-level transformers forzero-shot offensive span identification on a code-mixed Tamil dataset. Morespecifically, we evaluate rationale extraction methods of Local InterpretableModel Agnostic Explanations (LIME) \cite{DBLP:conf/kdd/Ribeiro0G16} andIntegrated Gradients (IG) \cite{DBLP:conf/icml/SundararajanTY17} for adaptingtransformer based offensive language classification models for zero-shotoffensive span identification. To this end, we find that LIME and IG showbaseline $F_{1}$ of 26.35\% and 44.83\%, respectively. Besides, we study theeffect of data set size and training process on the overall accuracy of spanidentification. As a result, we find both LIME and IG to show significantimprovement with Masked Data Augmentation and Multilabel Training, with $F_{1}$of 50.23\% and 47.38\% respectively. \textit{Disclaimer : This paper containsexamples that may be considered profane, vulgar, or offensive. The examples donot represent the views of the authors or their employers/graduate schoolstowards any person(s), group(s), practice(s), or entity/entities. Instead theyare used to emphasize only the linguistic research challenges.}

Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction