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            <namePart type="family">Gurevych</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.</abstract>
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%0 Conference Proceedings
%T SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
%A Yang, An
%A Li, Sujian
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yang-li-2018-scidtb
%X Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.
%R 10.18653/v1/P18-2071
%U https://aclanthology.org/P18-2071
%U https://doi.org/10.18653/v1/P18-2071
%P 444-449

Markdown (Informal)

[SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](https://aclanthology.org/P18-2071) (Yang & Li, ACL 2018)

• SciDTB: Discourse Dependency TreeBank for Scientific Abstracts (Yang & Li, ACL 2018)

ACL

• An Yang and Sujian Li. 2018. SciDTB: Discourse Dependency TreeBank for Scientific Abstracts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 444–449, Melbourne, Australia. Association for Computational Linguistics.