Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction. (arXiv:2205.06119v1 [cs.CL])

Web: http://arxiv.org/abs/2205.06119

This paper investigates the effectiveness of sentence-level transformers for
zero-shot offensive span identification on a code-mixed Tamil dataset. More
specifically, we evaluate rationale extraction methods of Local Interpretable
Model Agnostic Explanations (LIME) \cite{DBLP:conf/kdd/Ribeiro0G16} and
Integrated Gradients (IG) \cite{DBLP:conf/icml/SundararajanTY17} for adapting
transformer based offensive language classification models for zero-shot
offensive span identification. To this end, we find that LIME and IG show
baseline $F_{1}$ of 26.35\% and 44.83\%, respectively. Besides, we study the
effect of data set size and training process …

arxiv code extraction identification mixed