@inproceedings{miftahova-etal-2022-namedentityrangers,
title = "{N}amed{E}ntity{R}angers at {S}em{E}val-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition",
author = "Miftahova, Amina and
Pugachev, Alexander and
Skiba, Artem and
Artemova, Ekaterina and
Batura, Tatiana and
Braslavski, Pavel and
Ivanov, Vladimir",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.216",
doi = "10.18653/v1/2022.semeval-1.216",
pages = "1570--1575",
abstract = "This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual language models differently. The first approach follows the token classification schema, in which each token is assigned with a tag. The second approach follows a recent template-free paradigm, in which an encoder-decoder model translates the input sequence of words to a special output, encoding named entities with predefined labels. We utilize RemBERT and mT5 as backbone models for these two approaches, respectively. Our results show that the oldie but goodie token classification outperforms the template-free method by a wide margin. Our code is available at: \url{https://github.com/Abiks/MultiCoNER}.",
}
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<abstract>This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual language models differently. The first approach follows the token classification schema, in which each token is assigned with a tag. The second approach follows a recent template-free paradigm, in which an encoder-decoder model translates the input sequence of words to a special output, encoding named entities with predefined labels. We utilize RemBERT and mT5 as backbone models for these two approaches, respectively. Our results show that the oldie but goodie token classification outperforms the template-free method by a wide margin. Our code is available at: https://github.com/Abiks/MultiCoNER.</abstract>
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%0 Conference Proceedings
%T NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
%A Miftahova, Amina
%A Pugachev, Alexander
%A Skiba, Artem
%A Artemova, Ekaterina
%A Batura, Tatiana
%A Braslavski, Pavel
%A Ivanov, Vladimir
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F miftahova-etal-2022-namedentityrangers
%X This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual language models differently. The first approach follows the token classification schema, in which each token is assigned with a tag. The second approach follows a recent template-free paradigm, in which an encoder-decoder model translates the input sequence of words to a special output, encoding named entities with predefined labels. We utilize RemBERT and mT5 as backbone models for these two approaches, respectively. Our results show that the oldie but goodie token classification outperforms the template-free method by a wide margin. Our code is available at: https://github.com/Abiks/MultiCoNER.
%R 10.18653/v1/2022.semeval-1.216
%U https://aclanthology.org/2022.semeval-1.216
%U https://doi.org/10.18653/v1/2022.semeval-1.216
%P 1570-1575
Markdown (Informal)
[NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition](https://aclanthology.org/2022.semeval-1.216) (Miftahova et al., SemEval 2022)
ACL