Multi-Hop Fact Checking of Political Claims
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Recent work has proposed multi-hop models and datasets for studying complex natural language reasoning. One notable task requiring multi-hop reasoning is fact checking, where a set of connected evidence pieces leads to the final verdict of a claim. However, existing datasets either do not provide annotations for gold evidence pages, or the only dataset which does (FEVER) mostly consists of claims which can be fact-checked with simple reasoning and is constructed artificially. Here, we study more complex claim verification of naturally occurring claims with multiple hops over interconnected evidence chunks. We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop. We find that the task is complex and achieve the best performance with an architecture that specifically models reasoning over evidence pieces in combination with in-domain transfer learning.
Originalsprog | Engelsk |
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Titel | Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence |
Vol/bind | CoRR 2020 |
Forlag | International Joint Conferences on Artificial Intelligence |
Publikationsdato | 2021 |
Sider | 3892-3898 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 30th International Joint Conference on Artificial Intelligence - Montreal, Canada Varighed: 19 aug. 2021 → 27 aug. 2021 |
Konference
Konference | 30th International Joint Conference on Artificial Intelligence |
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Land | Canada |
By | Montreal |
Periode | 19/08/2021 → 27/08/2021 |
Navn | arXiv.org |
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Links
- https://arxiv.org/pdf/2009.06401.pdf
Forlagets udgivne version
ID: 299690304