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

TL;DR

This paper explores zero-shot offensive span detection in code-mixed Tamil using transformer models and rationale extraction methods, achieving significant improvements with data augmentation and multilabel training.

Contribution

It introduces the application of LIME and Integrated Gradients for zero-shot span identification in code-mixed language, demonstrating their effectiveness with data augmentation and multilabel training.

Findings

LIME and IG baseline F1 scores of 26.35% and 44.83%.

Data augmentation and multilabel training improve F1 to over 47%.

Significant enhancement in zero-shot offensive span detection performance.

Abstract

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 on the overall accuracy of span identification. As a result, we find both LIME and IG to show significant improvement with Masked Data Augmentation and Multilabel Training, with $F_{1}$ of 50.23\% and 47.38\% respectively. \textit{Disclaimer : This paper contains examples that may be considered profane, vulgar, or offensive. The examples do not represent the views of the authors or their employers/graduate schools towards any person(s), group(s), practice(s), or entity/entities. Instead they are used to emphasize only the linguistic research challenges.}