SciDTB: Discourse Dependency TreeBank for Scientific Abstracts
This work provides a new annotation corpus for scientific discourse, addressing a domain-specific need in NLP tasks like machine translation and question answering, but it is incremental as it builds on existing discourse annotation frameworks.
The authors introduced SciDTB, a domain-specific discourse treebank for scientific abstracts that uses dependency trees to represent discourse structure, and established it as a benchmark for evaluating discourse dependency parsers with provided baselines.
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.