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.}

本文研究了在混合代码泰米尔数据集上使用句子级变压器进行零 - shot 攻击性跨度识别的有效性，并评估了 LIME 和 IG 的基线性能，探究数据集大小和训练进程对跨度识别准确率的影响，并发现在 Masked 数据增强和多标签训练的情况下 LIME 和 IG 的性能得到了显著提高。