Policy Compliance Detection (PCD) is a task we encounter when reasoning over
texts, e.g. legal frameworks. Previous work to address PCD relies heavily on
modeling the task as a special case of Recognizing Textual Entailment.
Entailment is applicable to the problem of PCD, however viewing the policy as a
single proposition, as opposed to multiple interlinked propositions, yields
poor performance and lacks explainability. To address this challenge, more
recent proposals for PCD have argued for decomposing policies into expression
trees consisting of questions connected with logic operators. Question
answering is used to obtain answers to these questions with respect to a
scenario. Finally, the expression tree is evaluated in order to arrive at an
overall solution. However, this work assumes expression trees are provided by
experts, thus limiting its applicability to new policies. In this work, we
learn how to infer expression trees automatically from policy texts. We ensure
the validity of the inferred trees by introducing constrained decoding using a
finite state automaton to ensure the generation of valid trees. We determine
through automatic evaluation that 63% of the expression trees generated by our
constrained generation model are logically equivalent to gold trees. Human
evaluation shows that 88% of trees generated by our model are correct.

本文介绍了一种从政策文本中自动推断出表达树的方法，使用有限状态自动机确保生成的有效性，自动评估显示 63％的表达树与黄金树在逻辑上等价，而人工评估显示 88％的表达树正确。