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        <title>A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd</title>
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    <abstract>The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality. We present a method that breaks down a popular but relatively complex discourse-level argument annotation scheme into a simpler, iterative procedure that can be applied even by untrained annotators. We apply this method in a crowdsourcing setup and report on the reliability of the annotations obtained. The source code for a tool implementing our annotation method, as well as the sample data we obtained (4909 gold-standard annotations across 982 documents), are freely released to the research community. These are intended to serve the needs of qualitative research into argumentation, as well as of data-driven approaches to argument mining.</abstract>
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%0 Conference Proceedings
%T A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd
%A Miller, Tristan
%A Sukhareva, Maria
%A Gurevych, Iryna
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F miller-etal-2019-streamlined
%X The study of argumentation and the development of argument mining tools depends on the availability of annotated data, which is challenging to obtain in sufficient quantity and quality. We present a method that breaks down a popular but relatively complex discourse-level argument annotation scheme into a simpler, iterative procedure that can be applied even by untrained annotators. We apply this method in a crowdsourcing setup and report on the reliability of the annotations obtained. The source code for a tool implementing our annotation method, as well as the sample data we obtained (4909 gold-standard annotations across 982 documents), are freely released to the research community. These are intended to serve the needs of qualitative research into argumentation, as well as of data-driven approaches to argument mining.
%R 10.18653/v1/N19-1177
%U https://aclanthology.org/N19-1177
%U https://doi.org/10.18653/v1/N19-1177
%P 1790-1796

Markdown (Informal)

[A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd](https://aclanthology.org/N19-1177) (Miller et al., NAACL 2019)

• A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd (Miller et al., NAACL 2019)

ACL

• Tristan Miller, Maria Sukhareva, and Iryna Gurevych. 2019. A Streamlined Method for Sourcing Discourse-level Argumentation Annotations from the Crowd. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1790–1796, Minneapolis, Minnesota. Association for Computational Linguistics.