Abstract Meaning Representation for Paraphrase Detection

Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, Yi Chang


Abstract
Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal for paraphrase detection, a problem in which one is required to specify whether two sentences have the same meaning. We show that naïve use of AMR in paraphrase detection is not necessarily useful, and turn to describe a technique based on latent semantic analysis in combination with AMR parsing that significantly advances state-of-the-art results in paraphrase detection for the Microsoft Research Paraphrase Corpus. Our best results in the transductive setting are 86.6% for accuracy and 90.0% for F1 measure.
Anthology ID:
N18-1041
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:
442–452
Language:
URL:
https://aclanthology.org/N18-1041
DOI:
10.18653/v1/N18-1041
Bibkey:
Cite (ACL):
Fuad Issa, Marco Damonte, Shay B. Cohen, Xiaohui Yan, and Yi Chang. 2018. Abstract Meaning Representation for Paraphrase Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 442–452, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Abstract Meaning Representation for Paraphrase Detection (Issa et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1041.pdf