Offensive Language Identification in Transliterated and Code-Mixed
  Bangla
Md Nishat Raihan Umma Hani Tanmoy Anika Binte Islam Kai North Tharindu Ranasinghe Antonios Anastasopoulos Marcos Zampieri
Identifying offensive content in social media is vital for creating safe
online communities. Several recent studies have addressed this problem by
creating datasets for various languages. In this paper, we explore offensive
language identification in texts with transliterations and code-mixing,
linguistic phenomena common in multilingual societies, and a known challenge
for NLP systems. We introduce TB-OLID, a transliterated Bangla offensive
language dataset containing 5,000 manually annotated comments. We train and
fine-tune machine learning models on TB-OLID, and we evaluate their results on
this dataset. Our results show that English pre-trained transformer-based
models, such as fBERT and HateBERT achieve the best performance on this
dataset.