NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition

Amina Miftahova, Alexander Pugachev, Artem Skiba, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov


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.
Anthology ID:
2022.semeval-1.216
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1570–1575
Language:
URL:
https://aclanthology.org/2022.semeval-1.216
DOI:
10.18653/v1/2022.semeval-1.216
Bibkey:
Cite (ACL):
Amina Miftahova, Alexander Pugachev, Artem Skiba, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, and Vladimir Ivanov. 2022. NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1570–1575, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition (Miftahova et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.216.pdf
Video:
 https://aclanthology.org/2022.semeval-1.216.mp4
Code
 abiks/multiconer
Data
MultiCoNER