Verifying complex political claims is a challenging task, especially when
politicians use various tactics to subtly misrepresent the facts. Automatic
fact-checking systems fall short here, and their predictions like "half-true"
are not very useful in isolation, since we have no idea which parts of the
claim are true and which are not. In this work, we focus on decomposing a
complex claim into a comprehensive set of yes-no subquestions whose answers
influence the veracity of the claim. We present ClaimDecomp, a dataset of
decompositions for over 1000 claims. Given a claim and its verification
paragraph written by fact-checkers, our trained annotators write subquestions
covering both explicit propositions of the original claim and its implicit
facets, such as asking about additional political context that changes our view
of the claim's veracity. We study whether state-of-the-art models can generate
such subquestions, showing that these models generate reasonable questions to
ask, but predicting the comprehensive set of subquestions from the original
claim without evidence remains challenging. We further show that these
subquestions can help identify relevant evidence to fact-check the full claim
and derive the veracity through their answers, suggesting that they can be
useful pieces of a fact-checking pipeline.

本研究关注于将复杂的政治要求分解为全面的是非子问题，通过回答这些问题来判断政治要求的真实性，提供了 ClaimDecomp 数据集和一种训练好的注释方法来研究最先进的模型生成此类子问题，这些子问题可以帮助找到相关证据以审核整个要求并推导真实性。

生成针对复杂主张进行事实检查的字面和隐含子问题

Generating Literal and Implied Subquestions to Fact-check Complex Claims

Automatic fact-checking systems detect misinformation, such as fake news, by
(i) selecting check-worthy sentences for fact-checking, (ii) gathering related
information to the sentences, and (iii) inferring the factuality of the
sentences. Most prior research on (i) uses hand-crafted features to select
check-worthy sentences, and does not explicitly account for the recent finding
that the top weighted terms in both check-worthy and non-check-worthy sentences
are actually overlapping [15]. Motivated by this, we present a neural
check-worthiness sentence ranking model that represents each word in a sentence
by \textit{both} its embedding (aiming to capture its semantics) and its
syntactic dependencies (aiming to capture its role in modifying the semantics
of other terms in the sentence). Our model is an end-to-end trainable neural
network for check-worthiness ranking, which is trained on large amounts of
unlabelled data through weak supervision. Thorough experimental evaluation
against state of the art baselines, with and without weak supervision, shows
our model to be superior at all times (+13% in MAP and +28% at various
Precision cut-offs from the best baseline with statistical significance).
Empirical analysis of the use of weak supervision, word embedding pretraining
on domain-specific data, and the use of syntactic dependencies of our model
reveals that check-worthy sentences contain notably more identical syntactic
dependencies than non-check-worthy sentences.

本研究提出了基于神经网络的句子排名模型，在选择句子时考虑了其语法依赖和语义表达，并使用弱监督技术进行了大规模训练，结果表明模型的效果在多个评价指标上均优于现有基线方法，通过实证分析发现值得检查的句子中的语法依赖项相较于非检查句子具有明显的重叠规律。