Offensive Content Detection Via Synthetic Code-Switched Text


Publication date: October 17, 2022

Cesa Salaam, Franck Dernoncourt, Trung Bui, Danda Rawat, David Seunghyun Yoon

The prevalent use of offensive content in social media has become an important reason for concern for online platforms (customer service chat-boxes, social media platforms, etc). Classifying offensive and hate-speech content in on-line settings is an essential task in many applications that needs to be addressed accordingly. However, online text from online platforms can contain code-switching, a combination of more than one language. The non-availability of labeled code-switched data for low-resourced code-switching combinations adds difficulty to this problem. To overcome this, we release a real-world dataset containing around 10k samples for testing for three language combinations en-fr, en-es, and en-de1 and a synthetic code-switched textual dataset containing 30k samples for training2. In this paper, we describe the process for gathering the human-generated data and our algorithm for creating synthetic code-switched offensive content data. We also introduce the results of a keyword classification baseline and a multi-lingual transformer-based classification model.

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