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

• URL: http://arxiv.org/abs/2305.03300v1
• Date: Fri, 5 May 2023 06:05:45 GMT
• Title: LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa
• Authors: Rahul Mehta and Vasudeva Varma
• Abstract summary: This paper focuses on solving NER tasks in a multilingual setting for complex named entities. 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.
• Score: 13.062351454646912
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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