BUT-FIT At Semeval-2019 Task 7: Determining The Rumour Stance With Pre-trained Deep Bidirectional Transformers | Awesome LLM Papers

BUT-FIT At Semeval-2019 Task 7: Determining The Rumour Stance With Pre-trained Deep Bidirectional Transformers

Martin Fajcik, LukΓ‘Ε‘ Burget, Pavel Smrz Β· Proceedings of the 13th International Workshop on Semantic Evaluation Β· 2019

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. It finished at the 2nd place in the competition, without any hand-crafted features, only 0.2% behind the winner.

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