Detail publikace

BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers

FAJČÍK, M. BURGET, L. SMRŽ, P.

Originální název

BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

rumour stance, hidden rumour stance, BERT, transformer, classification, stance classification, twitter post classification, reddit post classification, thread post classification, semeval, rumoureval

Autoři

FAJČÍK, M.; BURGET, L.; SMRŽ, P.

Vydáno

26. 6. 2019

Nakladatel

Association for Computational Linguistics

Místo

Minneapolis, Minnesota

ISBN

978-1-950737-06-2

Kniha

Proceedings of the 13th International Workshop on Semantic Evaluation

Strany od

1097

Strany do

1104

Strany počet

8

URL

https://aclweb.org/anthology/papers/S/S19/S19-2192/

BibTex

@inproceedings{BUT158076,
  author="Martin {Fajčík} and Lukáš {Burget} and Pavel {Smrž}",
  title="BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers",
  booktitle="Proceedings of the 13th International Workshop on Semantic Evaluation",
  year="2019",
  pages="1097--1104",
  publisher="Association for Computational Linguistics",
  address="Minneapolis, Minnesota",
  isbn="978-1-950737-06-2",
  url="https://aclweb.org/anthology/papers/S/S19/S19-2192/"
}