Motivated by the promising performance of pre-trained language models, we
investigate BERT in an evidence retrieval and claim verification pipeline for
the FEVER fact extraction and verification challenge. To this end, we propose
to use two BERT models, one for retrieving potential evidence sentences
supporting or rejecting claims, and another for verifying claims based on the
predicted evidence sets. To train the BERT retrieval system, we use pointwise
and pairwise loss functions, and examine the effect of hard negative mining. A
second BERT model is trained to classify the samples as supported, refuted, and
not enough information. Our system achieves a new state of the art recall of
87.1 for retrieving top five sentences out of the FEVER documents consisting of
50K Wikipedia pages, and scores second in the official leaderboard with the
FEVER score of 69.7.

本研究针对 FEVER 事实抽取和验证挑战，探讨了使用预训练语言模型 BERT 实现证据检索和主张验证的方法，并使用 pointwise 和 pairwise 损失函数进行模型训练。实验结果表明，我们的系统在使用 50K Wikipedia 页面的 FEVER 文档进行前五个句子的检索时实现了 87.1 的最新召回率，并在官方排行榜中获得了 FEVER 得分 69.7 的第二名。

BERT 用于证据检索和主张验证

BERT for Evidence Retrieval and Claim Verification

We develop a system for the FEVER fact extraction and verification challenge
that uses a high precision entailment classifier based on transformer networks
pretrained with language modeling, to classify a broad set of potential
evidence. The precision of the entailment classifier allows us to enhance
recall by considering every statement from several articles to decide upon each
claim. We include not only the articles best matching the claim text by TFIDF
score, but read additional articles whose titles match named entities and
capitalized expressions occurring in the claim text. The entailment module
evaluates potential evidence one statement at a time, together with the title
of the page the evidence came from (providing a hint about possible pronoun
antecedents). In preliminary evaluation, the system achieves .5736 FEVER score,
.6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.

本研究开发了一种基于 transformer 网络预训练的高精度蕴含分类器，用于对 FEVER 事实提取和验证挑战中的广泛潜在证据进行分类，从而提高了召回率；在初步评估中，该系统在 FEVER 公共数据集上取得了.5736 的 FEVER 得分、.6108 的标签准确性和.6485 的证据 F1 值。