Machine translation (MT) is widely used to translate content on social media platforms aiming to improve accessibility. A great part of the content circulated on social media is user-generated and often contains non-standard spelling, hashtags, and emojis that pose challenges to MT systems. This leads to many mistranslated instances that are presented to users of these platforms, hindering their understanding of content written in other languages. Here, we pose that MT and potential mistranslations have an important and mostly under-explored impact on social media tasks such as sentiment analysis and offensive language identification. We create MT-Offense, a novel dataset containing OLID, the English offensive language detection dataset and its translations in five high and low-resource languages; Arabic, Hindi, Marathi, Sinhala, and Spanish produced by multiple open-access Neural Machine Translation systems. We provide scripts to evaluate the performance of various offensive language models on both original and MT content in different training and test set combinations.
@article{dmonte2024effects,
title={On the effects of machine translation on offensive language detection},
author={Dmonte, Alphaeus and Satapara, Shrey and Alsudais, Rehab and Ranasinghe, Tharindu and Zampieri, Marcos},
journal={Social Network Analysis and Mining},
volume={14},
number={1},
pages={242},
year={2024},
publisher={Springer}
}