Acquisition of Phrase Correspondences Using Natural Deduction Proofs

Hitomi Yanaka, Koji Mineshima, Pascual Martínez-Gómez, Daisuke Bekki


Abstract
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.
Anthology ID:
N18-1069
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
756–766
Language:
URL:
https://www.aclweb.org/anthology/N18-1069
DOI:
10.18653/v1/N18-1069
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1069.pdf