QANom: Question-Answer driven SRL for Nominalizations

Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Brook Weiss, Hangfeng He, Dan Roth, Luke Zettlemoyer, Ido Dagan

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


We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.
Original languageEnglish
Title of host publicationProceedings of the 28th International Conference on Computational Linguistics
EditorsChengqing Zong, Nuria Bel, Donia Scott
PublisherAssociation for Computational Linguistics (ACL)
Number of pages15
ISBN (Electronic)9781952148279
StatePublished - 1 Dec 2020
Event28th International Conference on Computational Linguistics, COLING 2020 - Virtual, Online, Spain
Duration: 8 Dec 202013 Dec 2020

Publication series

NameCOLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference


Conference28th International Conference on Computational Linguistics, COLING 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics


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