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。

ContractNLI: 合同文件级别自然语言推理数据集

ContractNLI: A Dataset for Document-level Natural Language Inference for  Contracts

Determining whether a given claim is supported by evidence is a fundamental
NLP problem that is best modeled as Textual Entailment. However, given a large
collection of text, finding evidence that could support or refute a given claim
is a challenge in itself, amplified by the fact that different evidence might
be needed to support or refute a claim. Nevertheless, most prior work decouples
evidence identification from determining the truth value of the claim given the
evidence.
We propose to consider these two aspects jointly. We develop TwoWingOS
(two-wing optimization strategy), a system that, while identifying appropriate
evidence for a claim, also determines whether or not the claim is supported by
the evidence. Given the claim, TwoWingOS attempts to identify a subset of the
evidence candidates; given the predicted evidence, it then attempts to
determine the truth value of the corresponding claim. We treat this challenge
as coupled optimization problems, training a joint model for it. TwoWingOS
offers two advantages: (i) Unlike pipeline systems, it facilitates
flexible-size evidence set, and (ii) Joint training improves both the claim
entailment and the evidence identification. Experiments on a benchmark dataset
show state-of-the-art performance. Code: this https URL

认定给定主张是否得到支持是一个基本的 NLP 问题，本文提出了一个名为 TwoWingOS 的系统，该系统通过联合处理适当的证据识别和支持或反驳该主张，将这两个方面结合起来进行了优化，实验结果表明其性能达到了最先进水平。