Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations

Vered Shwartz, Ido Dagan


Abstract
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.
Anthology ID:
P18-1111
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1200–1211
Language:
URL:
https://aclanthology.org/P18-1111
DOI:
10.18653/v1/P18-1111
Bibkey:
Cite (ACL):
Vered Shwartz and Ido Dagan. 2018. Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1200–1211, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations (Shwartz & Dagan, ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-1111.pdf
Presentation:
 P18-1111.Presentation.pdf
Video:
 https://vimeo.com/285803366
Code
 vered1986/panic