TY - GEN
T1 - NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
AU - Miftahova, Amina
AU - Pugachev, Alexander
AU - Skiba, Artem
AU - Artemova, Ekaterina
AU - Batura, Tatiana
AU - Braslavski, Pavel
AU - Ivanov, Vladimir
N1 - Funding Information:
The project is supported by the Russian Science Foundation, grant # 20-11-20166. This research was supported in part through computational resources of HPC facilities at HSE University (Kostenetskiy et al., 2021).
Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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 (Ma et al., 2021), 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.
AB - 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 (Ma et al., 2021), 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.
UR - http://www.scopus.com/inward/record.url?scp=85137590023&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85137590023
T3 - SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
SP - 1570
EP - 1575
BT - SemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
A2 - Emerson, Guy
A2 - Schluter, Natalie
A2 - Stanovsky, Gabriel
A2 - Kumar, Ritesh
A2 - Palmer, Alexis
A2 - Schneider, Nathan
A2 - Singh, Siddharth
A2 - Ratan, Shyam
PB - Association for Computational Linguistics (ACL)
T2 - 16th International Workshop on Semantic Evaluation, SemEval 2022
Y2 - 14 July 2022 through 15 July 2022
ER -