TY - GEN

T1 - Multi-Hop Fact Checking of Political Claims

AU - Ostrowski, Wojciech

AU - Arora, Arnav

AU - Atanasova, Pepa Kostadinova

AU - Augenstein, Isabelle

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.24963/ijcai.2021/536

DO - 10.24963/ijcai.2021/536

M3 - Article in proceedings

VL - CoRR 2020

T3 - arXiv.org

SP - 3892

EP - 3898

BT - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

PB - International Joint Conferences on Artificial Intelligence

T2 - 30th International Joint Conference on Artificial Intelligence

Y2 - 19 August 2021 through 27 August 2021

ER -