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Acquisition of Phrase Correspondences using Natural Deduction Proofs
lib:012da51b8d4db88c (v1.0.0)
Authors: Hitomi Yanaka,Koji Mineshima,Pascual Martinez-Gomez,Daisuke Bekki
Where published: NAACL 2018 6
ArXiv: 1804.07656
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1804.07656v1
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.
Where published: NAACL 2018 6
ArXiv: 1804.07656
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1804.07656v1
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.
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