Natural language inference (NLI) is a fundamental NLP task, investigating the
entailment relationship between two texts. Popular NLI datasets present the
task at sentence-level. While adequate for testing semantic representations,
they fall short for testing contextual reasoning over long texts, which is a
natural part of the human inference process. We introduce ConTRoL, a new
dataset for ConTextual Reasoning over Long texts. Consisting of 8,325
expert-designed "context-hypothesis" pairs with gold labels, ConTRoL is a
passage-level NLI dataset with a focus on complex contextual reasoning types
such as logical reasoning. It is derived from competitive selection and
recruitment test (verbal reasoning test) for police recruitment, with expert
level quality. Compared with previous NLI benchmarks, the materials in ConTRoL
are much more challenging, involving a range of reasoning types. Empirical
results show that state-of-the-art language models perform by far worse than
educated humans. Our dataset can also serve as a testing-set for downstream
tasks like Checking Factual Correctness of Summaries.

介绍一种新的基于长文本语境下的对比学习模型 ConTRoL，该模型具有一定难度，能够检测语境推理中的各种类型，并且在测试中结果表明该模型比自然语言处理的最先进模型表现得更好，同时还可进行事实正确性验证任务。