Minanto at SemEval-2023 Task 2: Fine-tuning XLM-RoBERTa for Named Entity Recognition on English Data
Antonia Höfer, Mina Mottahedin
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 2: multiconer ii multilingual complex named entity recognition Paper
TLDR:
Within the scope of the shared task MultiCoNER II our aim was to improve the recognition of named entities in English. We as team Minanto fine-tuned a cross-lingual model for Named Entity Recognition on English data and achieved an average F1 score of 51.47\textbackslash{}\% in the final submission.
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Abstract:
Within the scope of the shared task MultiCoNER II our aim was to improve the recognition of named entities in English. We as team Minanto fine-tuned a cross-lingual model for Named Entity Recognition on English data and achieved an average F1 score of 51.47\textbackslash{}\% in the final submission. We found that a monolingual model works better on English data than a cross-lingual and that the input of external data from earlier Named Entity Recognition tasks provides only minor improvements. In this paper we present our system, discuss our results and analyze the impact of external data.