How to identify, extract, and use phrasal knowledge is a crucial problem for
the task of Recognizing Textual Entailment (RTE). To solve this problem, we
propose a method for detecting paraphrases via natural deduction proofs of
semantic relations between sentence pairs. Our solution relies on a graph
reformulation of partial variable unifications and an algorithm that induces
subgraph alignments between meaning representations. Experiments show that our
method can automatically detect various paraphrases that are absent from
existing paraphrase databases. In addition, the detection of paraphrases using
proof information improves the accuracy of RTE tasks.

本研究提出一种通过语义关系自然推导证明来检测词组知识的方法，利用部分变量协同作图重构以及诱导意义呈现的子图对齐算法，可自动检测各种不存在于现有词组数据库的词组，进而提高了文本推理任务的准确率。

利用自然推理证明获取短语对应关系

Acquisition of Phrase Correspondences using Natural Deduction Proofs

Determining semantic textual similarity is a core research subject in natural
language processing. Since vector-based models for sentence representation
often use shallow information, capturing accurate semantics is difficult. By
contrast, logical semantic representations capture deeper levels of sentence
semantics, but their symbolic nature does not offer graded notions of textual
similarity. We propose a method for determining semantic textual similarity by
combining shallow features with features extracted from natural deduction
proofs of bidirectional entailment relations between sentence pairs. For the
natural deduction proofs, we use ccg2lambda, a higher-order automatic inference
system, which converts Combinatory Categorial Grammar (CCG) derivation trees
into semantic representations and conducts natural deduction proofs.
Experiments show that our system was able to outperform other logic-based
systems and that features derived from the proofs are effective for learning
textual similarity.

本文提出了一种通过将浅层特征和句子对之间的双向蕴涵关系的自然演绎证明的特征相结合的方法来确定语义文本相似度的方法，并使用 ccg2lambda 实现自动推理。实验表明，本方法能够超越其他逻辑方法，并且证明导出的特征对学习文本相似度很有效。