@inproceedings{roit-etal-2020-controlled,
title = "Controlled Crowdsourcing for High-Quality {QA}-{SRL} Annotation",
author = "Roit, Paul and
Klein, Ayal and
Stepanov, Daniela and
Mamou, Jonathan and
Michael, Julian and
Stanovsky, Gabriel and
Zettlemoyer, Luke and
Dagan, Ido",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.626",
doi = "10.18653/v1/2020.acl-main.626",
pages = "7008--7013",
abstract = "Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.",
}
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<abstract>Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.</abstract>
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%0 Conference Proceedings
%T Controlled Crowdsourcing for High-Quality QA-SRL Annotation
%A Roit, Paul
%A Klein, Ayal
%A Stepanov, Daniela
%A Mamou, Jonathan
%A Michael, Julian
%A Stanovsky, Gabriel
%A Zettlemoyer, Luke
%A Dagan, Ido
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F roit-etal-2020-controlled
%X Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen. Recently, a large-scale crowdsourced QA-SRL corpus and a trained parser were released. Trying to replicate the QA-SRL annotation for new texts, we found that the resulting annotations were lacking in quality, particularly in coverage, making them insufficient for further research and evaluation. In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase. Applying this protocol to QA-SRL yielded high-quality annotation with drastically higher coverage, producing a new gold evaluation dataset. We believe that our annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.
%R 10.18653/v1/2020.acl-main.626
%U https://aclanthology.org/2020.acl-main.626
%U https://doi.org/10.18653/v1/2020.acl-main.626
%P 7008-7013
Markdown (Informal)
[Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://aclanthology.org/2020.acl-main.626) (Roit et al., ACL 2020)
ACL
- Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, and Ido Dagan. 2020. Controlled Crowdsourcing for High-Quality QA-SRL Annotation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7008–7013, Online. Association for Computational Linguistics.