# SciDTB: Discourse Dependency TreeBank for Scientific Abstracts

Source: [SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](<https://www.sota2.com/research/paper/68fa64801e209131cea04e5f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:05:03.439Z

Authors: An Yang, Sujian Li

Published: 2018-06-10T13:00:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1806.03653>)

[PDF](<https://arxiv.org/pdf/1806.03653.pdf>)