Reviewing contracts is a time-consuming procedure that incurs large expenses
to companies and social inequality to those who cannot afford it. In this work,
we propose "document-level natural language inference (NLI) for contracts", a
novel, real-world application of NLI that addresses such problems. In this
task, a system is given a set of hypotheses (such as "Some obligations of
Agreement may survive termination.") and a contract, and it is asked to
classify whether each hypothesis is "entailed by", "contradicting to" or "not
mentioned by" (neutral to) the contract as well as identifying "evidence" for
the decision as spans in the contract. We annotated and release the largest
corpus to date consisting of 607 annotated contracts. We then show that
existing models fail badly on our task and introduce a strong baseline, which
(1) models evidence identification as multi-label classification over spans
instead of trying to predict start and end tokens, and (2) employs more
sophisticated context segmentation for dealing with long documents. We also
show that linguistic characteristics of contracts, such as negations by
exceptions, are contributing to the difficulty of this task and that there is
much room for improvement.

本研究提出了一种新的自然语言推理应用 —— 针对合同的 “文档级自然语言推理”，解决了合同审查过于耗时和过于昂贵的问题，并标注并发布了目前最大的包括 607 个合同的语料库。研究还指出，以往的模型在该任务上表现不佳，而研究者引入了一种新的算法，并通过多标签分类和更复杂的上下文分割方法使该任务取得了显著进展，并认为这一领域还有很大的改进空间。