@inproceedings{miftahutdinov-etal-2020-kfu,
title = "{KFU} {NLP} Team at {SMM}4{H} 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions",
author = "Miftahutdinov, Zulfat and
Sakhovskiy, Andrey and
Tutubalina, Elena",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.8/",
pages = "51--56",
abstract = "This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pretrained on a large-scale corpus of 5 million health-related user reviews in English and Russian. The ensemble of neural networks for extraction and normalization of adverse drug reactions ranked first among 7 teams at the SMM4H 2020 Task 3 and obtained a relaxed F1 of 46{\%}. The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58{\%} on English tweets and 51{\%} on Russian tweets."
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%0 Conference Proceedings
%T KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions
%A Miftahutdinov, Zulfat
%A Sakhovskiy, Andrey
%A Tutubalina, Elena
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F miftahutdinov-etal-2020-kfu
%X This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pretrained on a large-scale corpus of 5 million health-related user reviews in English and Russian. The ensemble of neural networks for extraction and normalization of adverse drug reactions ranked first among 7 teams at the SMM4H 2020 Task 3 and obtained a relaxed F1 of 46%. The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58% on English tweets and 51% on Russian tweets.
%U https://aclanthology.org/2020.smm4h-1.8/
%P 51-56
Markdown (Informal)
[KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions](https://aclanthology.org/2020.smm4h-1.8/) (Miftahutdinov et al., SMM4H 2020)
ACL