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# SciDTB: Discourse Dependency TreeBank for Scientific Abstracts

**Authors:** An Yang, Sujian Li · **Year:** 2018 · **Venue:** Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) **Link:** [aclanthology.org/P18-2071](https://aclanthology.org/P18-2071/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**42** citations](https://openalex.org/works?filter=cites:W2964113724) openalex-snapshot · 2026-10-14[**41** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/yang-2018-scidtb-discourse-dependency-treebank-scientific/) of the 140673 papers whose bibliographies we read[Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/P18-2/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/P18-2071/)
- [PDF](https://aclanthology.org/P18-2071.pdf)
- [DOI 10.18653/v1/p18-2071](https://doi.org/10.18653/v1/p18-2071)
- [OpenAlex](https://openalex.org/W2964113724)

**Authors (2)**

1. [An Yang](https://openalex.org/A5100459627) [ORCID](https://orcid.org/0000-0002-6529-1609) · h 25 · 2405 citations · 142 works
2. [Sujian Li](https://openalex.org/A5058353424) [ORCID](https://orcid.org/0000-0001-7493-0786) · h 46 · 7853 citations · 290 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The paper builds SciDTB, a discourse treebank of 798 ACL Anthology abstracts, annotated with dependency trees instead of RST constituency trees. Each elementary discourse unit (EDU) has one head and a labelled relation to its head, so crossing (non-projective) dependencies are possible. The relation set has 17 coarse and 26 fine types, and clauses are the default EDUs. EDUs were segmented by an automatic segmenter and then checked by hand; a second annotator changed only 28 of 2,772 EDUs. Between annotator pairs, the unlabelled attachment score (UAS) is about 0.75 to 0.81 and the labelled score (LAS) about 0.55 to 0.64. There are 39 non-projective trees (about 3%). Most relations (61.6%) join neighbouring EDUs. The paper's text gives 18,978 relations, while its size table lists 17,158. Three baseline parsers were trained on 492 abstracts. On the 152-abstract test set they score UAS 0.702 and LAS 0.535 (transition-based), and UAS 0.576 and LAS 0.425 (graph-based). Human agreement on the same test set is UAS 0.802 and LAS 0.622.

## Ideas

- I1. Use the attachment-distance table (61.6% adjacent, about 75% within one intermediate EDU) as a prior in a rule-based discourse attacher for Mova, and measure the gain on open texts.
- I2. Use the 17 coarse and 26 fine relation types as a checklist for [36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/). List the relations Mova cannot yet express, and mark the scientific ones (Background subtypes, Evidence, Goal) first.
- I3. Report discourse parsing as UAS and LAS, with the human agreement pair kept in the same table, so that the ceiling is visible.
- I4. Treat a unit split by a relative clause as one EDU with a same-unit link. This rule can be tested on the clause segmenter output of `en/src/parse.rs`.

## For Mova

The chapters are [36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/) and [37. Register and variation](https://md.mova.elamur.ai/free/en/docs/languages/en/37-register-and-variation/) (abstracts are a register). The code is `en/src/parse.rs`. The measure is UAS and LAS on a held-out set of open abstracts, run twice. The data are CC BY 4.0, so they can be used for building and testing.

Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/) · [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/p18-2071](https://doi.org/10.18653/v1/p18-2071)

```
@inproceedings{yang2018scidtb,
  title = {{SciDTB: Discourse Dependency TreeBank for Scientific Abstracts}},
  author = {Yang, An and Li, Sujian},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-2071},
  url = {https://doi.org/10.18653/v1/p18-2071}
}
```

- [← previous CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing](https://md.mova.elamur.ai/free/en/docs/papers/xue-2016-conll-2016-shared-task-multilingual/)
- [next → Slaapte or Sliep? Extending Neural-Network Simulations of English Past Tense Learning to Dutch and German](https://md.mova.elamur.ai/free/en/docs/papers/yang-2023-slaapte-or-sliep-extending-neural/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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