In an effort to assist factcheckers in the process of factchecking, we tackle
the claim detection task, one of the necessary stages prior to determining the
veracity of a claim. It consists of identifying the set of sentences, out of a
long text, deemed capable of being factchecked. This paper is a collaborative
work between Full Fact, an independent factchecking charity, and academic
partners. Leveraging the expertise of professional factcheckers, we develop an
annotation schema and a benchmark for automated claim detection that is more
consistent across time, topics and annotators than previous approaches. Our
annotation schema has been used to crowdsource the annotation of a dataset with
sentences from UK political TV shows. We introduce an approach based on
universal sentence representations to perform the classification, achieving an
F1 score of 0.83, with over 5% relative improvement over the state-of-the-art
methods ClaimBuster and ClaimRank. The system was deployed in production and
received positive user feedback.

本文介绍了利用专业事实核查人员的专业知识，开发了一种基于通用句子表示的方法来检测声明，其分类时的 F1 值高达 0.83，相对于现有方法 ClaimBuster 和 ClaimRank 提升了超过 5%。