SciDTB: Discourse Dependency TreeBank for Scientific Abstracts

An Yang, Sujian Li


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.
Anthology ID:
P18-2071
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
444–449
Language:
URL:
https://www.aclweb.org/anthology/P18-2071
DOI:
10.18653/v1/P18-2071
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2071.pdf
Video:
 https://vimeo.com/285804155
Presentation:
 P18-2071.Presentation.pdf