@inproceedings{davletov-etal-2020-gorynych,
title = "Gorynych Transformer at {S}em{E}val-2020 Task 6: Multi-task Learning for Definition Extraction",
author = "Davletov, Adis and
Arefyev, Nikolay and
Shatilov, Alexander and
Gordeev, Denis and
Rey, Alexey",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.59/",
doi = "10.18653/v1/2020.semeval-1.59",
pages = "487--493",
abstract = "This paper describes our approach to {\textquotedblleft}DeftEval: Extracting Definitions from Free Text in Textbooks{\textquotedblright} competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. DeftEval was split into three subtasks: sentence classification, sequence labeling and relation classification. Our solution ranked 5th in the first subtask and 23rd and 21st in the second and the third subtasks respectively. We applied simultaneous multi-task learning with Transformer-based models for subtasks 1 and 3 and a single BERT-based model for named entity recognition."
}
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<abstract>This paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. DeftEval was split into three subtasks: sentence classification, sequence labeling and relation classification. Our solution ranked 5th in the first subtask and 23rd and 21st in the second and the third subtasks respectively. We applied simultaneous multi-task learning with Transformer-based models for subtasks 1 and 3 and a single BERT-based model for named entity recognition.</abstract>
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%0 Conference Proceedings
%T Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction
%A Davletov, Adis
%A Arefyev, Nikolay
%A Shatilov, Alexander
%A Gordeev, Denis
%A Rey, Alexey
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F davletov-etal-2020-gorynych
%X This paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. DeftEval was split into three subtasks: sentence classification, sequence labeling and relation classification. Our solution ranked 5th in the first subtask and 23rd and 21st in the second and the third subtasks respectively. We applied simultaneous multi-task learning with Transformer-based models for subtasks 1 and 3 and a single BERT-based model for named entity recognition.
%R 10.18653/v1/2020.semeval-1.59
%U https://aclanthology.org/2020.semeval-1.59/
%U https://doi.org/10.18653/v1/2020.semeval-1.59
%P 487-493
Markdown (Informal)
[Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction](https://aclanthology.org/2020.semeval-1.59/) (Davletov et al., SemEval 2020)
ACL