HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification
Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, Mohit Bansal · 2020
We introduce HOVER (HOppy VERification), a dataset for many-hop evidence extraction and fact verification.It challenges models to extract facts from several Wikipedia articles that are relevant to a claim and classify whether the claim is SUPPORTED or NOT-SUPPORTED by the facts.In HOVER, the claims require evidence to be extracted from as many as four English Wikipedia articles and embody reasoning graphs of diverse shapes.Moreover, most of the 3/4-hop claims are written in multiple sentences, which adds to the complexity of understanding long-range dependency relations such as coreference.We show that the performance of an existing stateof-the-art semantic-matching model degrades significantly on our dataset as the number of reasoning hops increases, hence demonstrating the necessity of many-hop reasoning to achieve strong results.We hope that the introduction of this challenging dataset and the accompanying evaluation task will encourage research in many-hop fact retrieval and information verification.1 * Equal contribution. 1 We make HoVer dataset publicly available at https://hover-nlp.github.