@inproceedings{fajcik-etal-2019-fit,
title = "{BUT}-{FIT} at {S}em{E}val-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers",
author = "Fajcik, Martin and
Smrz, Pavel and
Burget, Lukas",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2192",
doi = "10.18653/v1/S19-2192",
pages = "1097--1104",
abstract = "This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67 {\%} on the provided test data. Without any hand-crafted feature, the system finished at the 2nd place in the competition, only 0.2 {\%} behind the winner.",
}
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<abstract>This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67 % on the provided test data. Without any hand-crafted feature, the system finished at the 2nd place in the competition, only 0.2 % behind the winner.</abstract>
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%0 Conference Proceedings
%T BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
%A Fajcik, Martin
%A Smrz, Pavel
%A Burget, Lukas
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F fajcik-etal-2019-fit
%X This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67 % on the provided test data. Without any hand-crafted feature, the system finished at the 2nd place in the competition, only 0.2 % behind the winner.
%R 10.18653/v1/S19-2192
%U https://aclanthology.org/S19-2192
%U https://doi.org/10.18653/v1/S19-2192
%P 1097-1104
Markdown (Informal)
[BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers](https://aclanthology.org/S19-2192) (Fajcik et al., SemEval 2019)
ACL