@inproceedings{hofer-mottahedin-2023-minanto,
title = "Minanto at {S}em{E}val-2023 Task 2: Fine-tuning {XLM}-{R}o{BERT}a for Named Entity Recognition on {E}nglish Data",
author = {H{\"o}fer, Antonia and
Mottahedin, Mina},
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.156",
doi = "10.18653/v1/2023.semeval-1.156",
pages = "1127--1130",
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.",
}
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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.</abstract>
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%0 Conference Proceedings
%T Minanto at SemEval-2023 Task 2: Fine-tuning XLM-RoBERTa for Named Entity Recognition on English Data
%A Höfer, Antonia
%A Mottahedin, Mina
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F hofer-mottahedin-2023-minanto
%X 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.
%R 10.18653/v1/2023.semeval-1.156
%U https://aclanthology.org/2023.semeval-1.156
%U https://doi.org/10.18653/v1/2023.semeval-1.156
%P 1127-1130
Markdown (Informal)
[Minanto at SemEval-2023 Task 2: Fine-tuning XLM-RoBERTa for Named Entity Recognition on English Data](https://aclanthology.org/2023.semeval-1.156) (Höfer & Mottahedin, SemEval 2023)
ACL