LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa
This work addresses multilingual NER for complex entities, but it is incremental as it applies an existing method to a new multilingual benchmark.
The paper tackled multilingual complex named entity recognition (NER) by fine-tuning XLM-RoBERTa on datasets from 12 languages, achieving participation in the SemEval-2023 MultiCoNER II task.
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