论文标题
用于自动事实提取和验证的分层证据设置建模
Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification
论文作者
论文摘要
自动化事实提取和验证是一项具有挑战性的任务，涉及从可靠的语料库中找到相关的证据句子来验证索赔的真实性。现有模型（i）将所有证据句子串联，导致包含多余和嘈杂的信息；或（ii）处理每个索赔证明句子对分别进行处理，然后将所有句子汇总为所有句子，缺少相关句子的早期组合以进行更准确的索赔验证。与先前的作品不同，在本文中，我们提出了层次证据集建模（HESM），这是一个提取证据集的框架（每个框架可能包含多个证据句子），并通过编码和参加不同级别等级的索赔和证据集来验证索赔，被驳斥或不足的信息，通过编码和参加索赔和证据集。我们的实验结果表明，HESM的表现优于7种最先进的方法来提取事实和要求验证。我们的源代码可在https://github.com/shyamsubramanian/hesm上找到。
Automated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim. Existing models either (i) concatenate all the evidence sentences, leading to the inclusion of redundant and noisy information; or (ii) process each claim-evidence sentence pair separately and aggregate all of them later, missing the early combination of related sentences for more accurate claim verification. Unlike the prior works, in this paper, we propose Hierarchical Evidence Set Modeling (HESM), a framework to extract evidence sets (each of which may contain multiple evidence sentences), and verify a claim to be supported, refuted or not enough info, by encoding and attending the claim and evidence sets at different levels of hierarchy. Our experimental results show that HESM outperforms 7 state-of-the-art methods for fact extraction and claim verification. Our source code is available at https://github.com/ShyamSubramanian/HESM.
