Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification

Anton Chernyavskiy, Dmitry Ilvovsky


Abstract
Triggered by Internet development, a large amount of information is published in online sources. However, it is a well-known fact that publications are inundated with inaccurate data. That is why fact-checking has become a significant topic in the last 5 years. It is widely accepted that factual data verification is a challenge even for the experts. This paper presents a domain-independent fact checking system. It can solve the fact verification problem entirely or at the individual stages. The proposed model combines various advanced methods of text data analysis, such as BERT and Infersent. The theoretical and empirical study of the system features is carried out. Based on FEVER and Fact Checking Challenge test-collections, experimental results demonstrate that our model can achieve the score on a par with state-of-the-art models designed by the specificity of particular datasets.
Anthology ID:
D19-6612
Volume:
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
69–78
Language:
URL:
https://www.aclweb.org/anthology/D19-6612
DOI:
10.18653/v1/D19-6612
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6612.pdf