{"type":"ScholarlyArticle","title":"Multi-Hop Fact Checking of Political Claims","authors":[{"name":"Wojciech Ostrowski","slug":"wojciech-ostrowski","given":"Wojciech","family":"Ostrowski","affiliation":""},{"name":"Arnav Arora","slug":"arnav-arora","given":"Arnav","family":"Arora","affiliation":""},{"name":"Pepa Atanasova","slug":"pepa-atanasova","given":"Pepa","family":"Atanasova","affiliation":""},{"name":"Isabelle Augenstein","slug":"isabelle-augenstein","given":"Isabelle","family":"Augenstein","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/536","url":"https://ijcai.org/proceedings/2021/536","pdf":"https://ijcai.org/proceedings/2021/0536.pdf","markdown":"https://ijcai.org/proceedings/2021/536.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/536","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Natural Language Processing","track":"Main Track","number":536,"pages":{"first":3892,"last":3898},"keywords":["Natural Language Processing: Text Classification","Natural Language Processing: Resources and Evaluation","Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}