Abstract
We present Transformer based pretrained models, which are fine-tuned for Named Entity Recognition (NER) task. Our team participated in SemEval-2022 Task 11 MultiCoNER: Multilingual Complex Named Entity Recognition task for Hindi and Bangla. Result comparison of six models (mBERT, IndicBERT, MuRIL (Base), MuRIL (Large), XLM-RoBERTa (Base) and XLM-RoBERTa (Large) ) has been performed. It is found that among these models MuRIL (Large) model performs better for both the Hindi and Bangla languages. Its F1-Scores for Hindi and Bangla are 0.69 and 0.59 respectively.- Anthology ID:
- 2022.semeval-1.211
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1536–1542
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.211
- DOI:
- 10.18653/v1/2022.semeval-1.211
- Cite (ACL):
- Sumit Singh, Pawankumar Jawale, and Uma Tiwary. 2022. silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1536–1542, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages (Singh et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.semeval-1.211.pdf