WiCE：维基百科中主张的现实世界蕴含
WiCE: Real-World Entailment for Claims in Wikipedia
AI总结：
提出了WiCE，一个基于Wikipedia自然主张和证据对的细粒度文本蕴含数据集，包含子句级蕴含判断和最小证据子集，并利用GPT-3.5自动分解策略提升模型性能，揭示了真实主张的验证与检索挑战。
摘要：
文本蕴含模型正越来越多地应用于事实核查、问答中的预设验证或摘要评估等场景。然而，这与现有的蕴含数据集存在显著的领域偏移，导致模型性能下降。我们提出了WiCE，这是一个新的细粒度文本蕴含数据集，基于从Wikipedia提取的自然主张和证据对构建。除了标准的声明级蕴含外，WiCE还提供了对主张子句单元的蕴含判断，以及支持每个子主张的最小证据句子子集。为此，我们提出了一种使用GPT-3.5的自动主张分解策略，我们表明该策略在测试时也能有效提升蕴含模型在多个数据集上的性能。最后，我们展示了数据集中的真实主张涉及现有模型无法解决的具有挑战性的验证和检索问题。
英文摘要：
Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represent a significant domain shift from existing entailment datasets, and models underperform as a result. We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim, and a minimal subset of evidence sentences that support each subclaim. To support this, we propose an automatic claim decomposition strategy using GPT-3.5 which we show is also effective at improving entailment models' performance on multiple datasets at test time. Finally, we show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
