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# How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT

**Authors:** [Mathieu Morey](https://md.mova.elamur.ai/free/en/docs/papers/authors/mathieu-morey/), [Philippe Muller](https://md.mova.elamur.ai/free/en/docs/papers/authors/philippe-muller/) and [Nicholas Asher](https://md.mova.elamur.ai/free/en/docs/papers/authors/nicholas-asher/) · **Year:** 2017 · **Venue:** Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing **Link:** [aclanthology.org/D17-1136](https://aclanthology.org/D17-1136/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology); the RST-DT text is LDC material, so use it for evaluation only

[**76** citations](https://openalex.org/works?filter=cites:W2758822623) openalex · 2026-10-18[**50** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/morey-2017-how-much-progress-have-we/) of the 140673 papers whose bibliographies we read[Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D17-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/D17-1136/)
- [PDF](https://aclanthology.org/D17-1136.pdf)
- [DOI 10.18653/v1/d17-1136](https://doi.org/10.18653/v1/d17-1136)
- [OpenAlex](https://openalex.org/W2758822623)

**Authors (3)**

1. [Mathieu Morey](https://openalex.org/A5013170838) [ORCID](https://orcid.org/0000-0002-7403-5359) · h 8 · 417 citations · 24 works
2. [Philippe Muller](https://openalex.org/A5005803234) [ORCID](https://orcid.org/0000-0002-6765-4020) · h 18 · 1325 citations · 164 works
3. [Nicholas Asher](https://openalex.org/A5019863253) [ORCID](https://orcid.org/0000-0002-7689-8246) · h 52 · 11248 citations · 474 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

Eight papers from 2013 to 2017 reported large gains in RST discourse parsing, with a claimed relative error reduction of 24 to 51%. The gains were attributed to distributed (neural) representations of discourse units. The authors asked the authors of nine parsers for their test-set predictions on the RST-DT, which has 385 articles (347 for training, 38 for test), and re-scored them. Their main finding is that most of the reported gains come from evaluation differences. The neural parsers were scored with macro-averaged F1 across documents, while the older parsers used micro-averaged F1. Under the same micro-averaged procedure the neural group loses its lead on the span and nuclearity scores. Its best gain on relations and full labels is 0.9 and 1.4 points, a relative error reduction of 11% and 16%. A second check used the standard Parseval procedure on right-heavy binarised trees instead of RST-Parseval. Under it the best full-label F1 is 46.3, and the gains are at most 16% relative error reduction.

## Ideas

- I1. `coref/src/eval.rs` and any future discourse scorer: always report micro and macro averages side by side, computed from BTreeMap-ordered per-document counts, and refuse a claim of gain unless both are stated. The paper's whole finding depends on this.
- I2. A replication step before any claim of progress: keep the previous run's predictions and re-score them with the new scorer. A gain that appears only with the new scorer is an evaluation artefact, the paper's main lesson.
- I3. Add a standard Parseval mode for Mova's own discourse trees: right-heavy binarisation of the reference, then labelled attachment decisions. Compare it with the native scorer on the same output to see how much the scorer matters.
- I4. Keep a simple feature baseline (unit length, distance between units, word and POS n-grams, as in the paper's first group) in every discourse measurement. The paper shows such a baseline is competitive, so a new rule system must beat it on the same scorer.

## For Mova

The chapters are [36. Discourse relations and coherence](https://md.mova.elamur.ai/free/en/docs/languages/en/36-discourse-relations/) (relations and structure) and [38. Dialogue, discourse markers and reported speech](https://md.mova.elamur.ai/free/en/docs/languages/en/38-dialogue-and-discourse-markers/) (markers). The code is `coref/src/eval.rs` as the model for scorer conventions, and `prag/src/frames.rs` for the discourse layer. Mova has no RST parser in the repo; if one is built, the measure is the paper's table layout (S, N, R, F) with both averages, run twice, plus the replication check against the old predictions. The RST-DT is evaluation-only data.

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

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/d17-1136](https://doi.org/10.18653/v1/d17-1136)

```
@inproceedings{morey2017how,
  title = {{How much progress have we made on RST discourse parsing? A replication study of recent results on the RST-DT}},
  author = {Morey, Mathieu and Muller, Philippe and Asher, Nicholas},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1136},
  url = {https://doi.org/10.18653/v1/d17-1136}
}
```

- [← previous Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection](https://md.mova.elamur.ai/free/en/docs/papers/moosavi-2019-automatically-extracted-minimum-spans-disentangle/)
- [next → A Dependency Perspective on RST Discourse Parsing and Evaluation](https://md.mova.elamur.ai/free/en/docs/papers/morey-2018-dependency-perspective-rst-discourse-parsing/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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