Abstract
The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been published in the last few years, with promising results. The majority of these studies, however, deal with high-resource languages such as English due to the availability of datasets in these languages. Recent work has addressed offensive language identification from a low-resource perspective, exploring data augmentation strategies and trying to take advantage of existing multilingual pretrained models to cope with data scarcity in low-resource scenarios. In this work, we revisit the problem of low-resource offensive language identification by evaluating the performance of multilingual transformers in offensive language identification for languages spoken in India. We investigate languages from different families such as Indo-Aryan (e.g., Bengali, Hindi, and Urdu) and Dravidian (e.g., Tamil, Malayalam, and Kannada), creating important new technology for these languages. The results show that multilingual offensive language identification models perform better than monolingual models and that cross-lingual transformers show strong zero-shot and few-shot performance across languages.
Highlights
Computational models trained to identify various types of offensive content online have been widely studied in recent years [1]
We explored multilingual offensive language identification with transformers for six languages spoken in India
We observed that multilingual offensive language identification models provide strong results on the language pairs they were trained in
Summary
Computational models trained to identify various types of offensive content online (e.g., hate speech, cyberbullying) have been widely studied in recent years [1]. Taking advantage of recent advances in deep learning representation such as context word embeddings and multilingual transformers in the past several years, a few studies have been published on multilingual models applied to offensive language identification [6,7,8]. This has opened new avenues for offensive language identification in low-resource languages. We investigate the use of multilingual models to offensive language identification for six languages spoken in India. We explore three main scenarios: (1) zero-shot learning, when a target language does not have any examples; (2) few-shot learning, when a target language has limited training examples, that is, fewer instances than the full training dataset for that language; (3) cross-lingual learning, when the full size target language training set is used regardless of the training set size
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