• Corpus ID: 237355019

BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding

@inproceedings{Bhattacharjee2021BanglaBERTCE,
  title={BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding},
  author={Abhik Bhattacharjee and Tahmid Hasan and Kazi Samin and Md Saiful Islam and M. Sohel Rahman and Anindya Iqbal and Rifat Shahriyar},
  year={2021}
}
In this paper, we introduce “Embedding Barrier”, a phenomenon that limits the monolingual performance of multilingual models on low-resource languages having unique typologies. We build ‘BanglaBERT’, a Bangla language model pretrained on 18.6 GB Internetcrawled data and benchmark on five standard NLU tasks. We discover a significant drop in the performance of the stateof-the-art multilingual model (XLM-R) from BanglaBERT and attribute this to the Embedding Barrier through comprehensive… 
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