The increased focus on misinformation has spurred development of data and
systems for detecting the veracity of a claim as well as retrieving
authoritative evidence. The Fact Extraction and VERification (FEVER) dataset
provides such a resource for evaluating end-to-end fact-checking, requiring
retrieval of evidence from Wikipedia to validate a veracity prediction. We show
that current systems for FEVER are vulnerable to three categories of realistic
challenges for fact-checking -- multiple propositions, temporal reasoning, and
ambiguity and lexical variation -- and introduce a resource with these types of
claims. Then we present a system designed to be resilient to these "attacks"
using multiple pointer networks for document selection and jointly modeling a
sequence of evidence sentences and veracity relation predictions. We find that
in handling these attacks we obtain state-of-the-art results on FEVER, largely
due to improved evidence retrieval.

本研究探讨了当前的事实检查系统面临的挑战，介绍了一种新的对抗性数据集，并提出了一种新的系统来处理多种类型的谎言，通过多个指针网络进行文档选择，并联合建模一系列证据句子和真实性关系预测，具有更优异的证据检索表现。