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.
Original language | English |
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Title of host publication | ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 7008-7013 |
Number of pages | 6 |
ISBN (Electronic) | 9781952148255 |
State | Published - 2020 |
Event | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States Duration: 5 Jul 2020 → 10 Jul 2020 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Conference
Conference | 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 5/07/20 → 10/07/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics