Language Identification of Hindi-English tweets using code-mixed BERT
Abstract
Language identification of social media text has been an interesting problem of study in recent years. Social media messages are predominantly in code mixed in non-English speaking states. Prior knowledge by pre-training contextual embeddings have shown state of the art results for a range of downstream tasks. Recently, models such as BERT have shown that using a large amount of unlabeled data, the pretrained language models are even more beneficial for learning common language representations. Extensive experiments exploiting transfer learning and fine-tuning BERT models to identify language on Twitter are presented in this paper. The work utilizes a data collection of Hindi-English-Urdu codemixed text for language pre-training and Hindi-English codemixed for subsequent word-level language classification. The results show that the representations pre-trained over codemixed data produce better results by their monolingual counterpart.
Cite
@article{arxiv.2107.01202,
title = {Language Identification of Hindi-English tweets using code-mixed BERT},
author = {Mohd Zeeshan Ansari and M M Sufyan Beg and Tanvir Ahmad and Mohd Jazib Khan and Ghazali Wasim},
journal= {arXiv preprint arXiv:2107.01202},
year = {2021}
}