Paraphrase to explicate: Revealing implicit noun-compound relations

Vered Shwartz, Ido Dagan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

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.

Original languageEnglish
Title of host publicationACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages1200-1211
Number of pages12
ISBN (Electronic)9781948087322
DOIs
StatePublished - 2018
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018

Publication series

NameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume1

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/TerritoryAustralia
CityMelbourne
Period15/07/1820/07/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computational Linguistics

Funding

This work was supported in part by an Intel ICRI-CI grant, the Israel Science Foundation grant 1951/17, the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1), and Theo Hoffenberg. Vered is also supported by the Clore Scholars Pro-gramme (2017), and the AI2 Key Scientific Challenges Program (2017). This work was supported in part by an Intel ICRI-CI grant, the Israel Science Foundation grant 1951/17, the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1), and Theo Hoffenberg. Vered is also supported by the Clore Scholars Programme (2017), and the AI2 Key Scientific Challenges Program (2017).

FundersFunder number
AI2 Key Scientific Challenges Program
DIPDA 1600/1-1
German-Israeli Project Cooperation
Intel ICRI-CI
Deutsche Forschungsgemeinschaft1600/1-1
Israel Science Foundation1951/17

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