nli-fever

This dataset has been proposed in Combining fact extraction and verification with neural semantic matching networks
In the original FEVER setting, the input is a claim from Wikipedia and the expected output is a label. However, this is different from the standard NLI formalization which is basically a pair-of-sequence to label problem. To facilitate NLI-related research to take advantage of the FEVER dataset, the authors pair the claims in the FEVER dataset with the textual evidence and make it a pair-of-sequence to label formatted dataset.

本数据集由《结合事实抽取与验证的神经语义匹配网络》（Combining fact extraction and verification with neural semantic matching networks）一文提出。在原始FEVER数据集设定中，输入为来自维基百科的断言，预期输出为分类标签。然而，该设定与标准自然语言推理（Natural Language Inference, NLI）的形式化范式存在差异，标准自然语言推理本质上属于序列对到标签的任务。为了便于相关自然语言推理研究利用FEVER数据集，该文作者将FEVER数据集中的断言与文本证据进行配对，将其构建为符合序列对到标签格式的数据集。