EMNLP 2021, 2021 Conference on Empirical Methods in Natural Language Processing, FEVEROUS Workshop, 7-11 November 2021, Punta Cana, Dominican Republic
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information)
challenge which consists of an open source baseline system together with a benchmark
dataset containing 87,026 verified claims. We extend this baseline model by improving
the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).
Copyright ACL. Personal use of this material is permitted. The definitive version of this paper was published in EMNLP 2021, 2021 Conference on Empirical Methods in Natural Language Processing, FEVEROUS Workshop, 7-11 November 2021, Punta Cana, Dominican Republic and is available at : http://dx.doi.org/10.18653/v1/2021.fever-1.12