Abstract
There has been an increasing attention to the task of fact checking. Among others, FEVER is a recently popular fact verification task in which a system is supposed to extract information from given Wikipedia documents and verify the given claim. In this paper, we present a four-stage model for this task including document retrieval, sentence selection, evidence sufficiency judgement and claim verification. Different from most existing models, we design a new evidence sufficiency judgement model to judge the sufficiency of the evidences for each claim and control the number of evidences dynamically. Experiments on FEVER show that our model is effective in judging the sufficiency of the evidence set and can get a better evidence F1 score with a comparable claim verification performance.
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Notes
- 1.
The evidences provided in the FEVER dataset.
- 2.
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Acknowledgment
This work is supported in part by the NSFC (Grant No.61672057, 61672058, 61872294), the National Hi-Tech R&D Program of China (No. 2018YFC0831900). For any correspondence, please contact Yansong Feng.
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Lin, Y., Huang, P., Lai, Y., Feng, Y., Zhao, D. (2019). Evidence Distilling for Fact Extraction and Verification. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_17
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DOI: https://doi.org/10.1007/978-3-030-32233-5_17
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