@inproceedings{klein-etal-2020-qanom,
title = "{QAN}om: Question-Answer driven {SRL} for Nominalizations",
author = "Klein, Ayal and
Mamou, Jonathan and
Pyatkin, Valentina and
Stepanov, Daniela and
He, Hangfeng and
Roth, Dan and
Zettlemoyer, Luke and
Dagan, Ido",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.274",
doi = "10.18653/v1/2020.coling-main.274",
pages = "3069--3083",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T QANom: Question-Answer driven SRL for Nominalizations
%A Klein, Ayal
%A Mamou, Jonathan
%A Pyatkin, Valentina
%A Stepanov, Daniela
%A He, Hangfeng
%A Roth, Dan
%A Zettlemoyer, Luke
%A Dagan, Ido
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F klein-etal-2020-qanom
%X 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.
%R 10.18653/v1/2020.coling-main.274
%U https://aclanthology.org/2020.coling-main.274
%U https://doi.org/10.18653/v1/2020.coling-main.274
%P 3069-3083
Markdown (Informal)
[QANom: Question-Answer driven SRL for Nominalizations](https://aclanthology.org/2020.coling-main.274) (Klein et al., COLING 2020)
ACL
- Ayal Klein, Jonathan Mamou, Valentina Pyatkin, Daniela Stepanov, Hangfeng He, Dan Roth, Luke Zettlemoyer, and Ido Dagan. 2020. QANom: Question-Answer driven SRL for Nominalizations. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3069–3083, Barcelona, Spain (Online). International Committee on Computational Linguistics.