LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa

Rahul Mehta, Vasudeva Varma


Abstract
Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities. Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.
Anthology ID:
2023.semeval-1.62
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
453–456
Language:
URL:
https://aclanthology.org/2023.semeval-1.62
DOI:
10.18653/v1/2023.semeval-1.62
Bibkey:
Cite (ACL):
Rahul Mehta and Vasudeva Varma. 2023. LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 453–456, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa (Mehta & Varma, SemEval 2023)
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PDF:
https://aclanthology.org/2023.semeval-1.62.pdf