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

Rahul Mehta, Vasudeva Varma
5/5/2023
cs.CL

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Abstract

Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity Recognition. We approach the problem by leveraging cross-lingual representation provided by fine-tuning XLM-Roberta base model on datasets of all of the 12 languages provided -- Bangla, Chinese, English, Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and Ukrainian

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Cite this paper

@article{mehta2023llmrm,
  title  = {LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa},
  author = {Rahul Mehta and Vasudeva Varma},
  year   = {2023},
  eprint = {2305.03300},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.03300v1}
}

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