# Acquisition of Phrase Correspondences using Natural Deduction Proofs

arXiv: https://arxiv.org/abs/1804.07656 · HTML: https://mlsanity.com/paper/1804.07656/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning](https://mlsanity.com/paper/2105.14167/) (2021)
- [Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models](https://mlsanity.com/paper/2205.11432/) (2022)
- [RECKONING: Reasoning through Dynamic Knowledge Encoding](https://mlsanity.com/paper/2305.06349/) (2023)
- [Probing Linguistic Information For Logical Inference In Pre-trained Language Models](https://mlsanity.com/paper/2112.01753/) (2021)
- [When Does Meaning Backfire? Investigating the Role of AMRs in NLI](https://mlsanity.com/paper/2506.14613/) (2025)
- [A Logic-Based Framework for Natural Language Inference in Dutch](https://mlsanity.com/paper/2110.03323/) (2021)
- [Linguistic Features for Interpretable Textual Entailment](https://mlsanity.com/paper/2609.24932/) (2026)
- [Explaining Textual Entailment with Lexical Entailments: Using LLMs to Supply Lexical Relations for Formal Proofs](https://mlsanity.com/paper/2609.32491/) (2026)
- [Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI](https://mlsanity.com/paper/2610.15158/) (2026)

## Abstract (arXiv)

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.