---
title: "Multilingual Neural RST Discourse Parsing — Liu 2020"
description: "Most work on RST discourse parsing builds and tests models on the English RST-DT."
authors: ["Zhengyuan Liu", "Ke Shi", "Nancy Chen"]
year: 2020
venue: "Proceedings of the 28th International Conference on Computational Linguistics"
source: "https://aclanthology.org/2020.coling-main.591/"
doi: "10.18653/v1/2020.coling-main.591"
arxiv: "2012.01704"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/liu-2020-multilingual-neural-rst-discourse-parsing/"
---
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# Multilingual Neural RST Discourse Parsing

**Authors:** [Zhengyuan Liu](https://md.research.elamur.ai/free/authors/zhengyuan-liu/), [Ke Shi](https://md.research.elamur.ai/free/authors/ke-shi/), [Nancy Chen](https://md.research.elamur.ai/free/authors/nancy-chen/) · **Year:** 2020 · **Venue:** Proceedings of the 28th International Conference on Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.coling-main.591/) · [PDF](https://aclanthology.org/2020.coling-main.591.pdf) · [DOI 10.18653/v1/2020.coling-main.591](https://doi.org/10.18653/v1/2020.coling-main.591) · [arXiv 2012.01704](https://arxiv.org/abs/2012.01704) · [OpenAlex](https://openalex.org/W3116040647)

**Citations:** 19 citations (openalex-snapshot · 2026-10-14) · [18](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

Most work on RST discourse parsing builds and tests models on the English RST-DT. Other languages have even smaller annotated treebanks, so parsers for them perform poorly. The authors build one cross-lingual neural parser for documents. They base it on the top-down encoder-decoder design of Lin et al. (2019), which they extend from single sentences to whole documents. It encodes EDUs hierarchically, splits spans with a pointer-network decoder and a stack, and labels nuclearity and relation jointly with a bi-affine classifier.

They test two ways of handling many languages:
- **Shared multilingual embeddings:** a fine-tuned XLM-RoBERTa encoder places all languages in one representation space.
- **Translation unit by unit:** each EDU is machine-translated into English separately, so the segmentation and tree annotations stay intact.

Experiments cover treebanks in six languages. When the parser is trained on all of them together, both strategies beat earlier cross-lingual systems on span, nuclearity and relation prediction, and span scores come close to human agreement. This shows that pooling small RST resources across languages is a workable answer to the shortage of annotated data.

## Key points

- **Data:** six languages, with treebanks unified following Braud et al. (2017).
  - English RST-DT (385 documents)
  - Portuguese: CST-News, Summ-it, Rhetalho, CorpusTCC
  - Spanish RST-DT
  - German MAZ Corpus
  - Dutch RST-DT
  - Basque RST-DT
- **Setup:** relations were grouped into 18 classes combined with the NS/SN/NN nuclearity labels. Each language contributes 38 test documents, giving about 1.1k training and 228 test documents in total.
- **Long inputs:** documents can exceed 2048 sub-word tokens, so XLM-RoBERTa reads them with a sliding window (size 500, stride 200).
- **Which method wins where:** shared embeddings do slightly better on the smallest treebanks (De-DT, Nl-DT, Eu-DT). EDU-level translation gives the best English results.
- **Topic analysis:** an LDA and t-SNE study shows that the treebanks cover different domains; En-DT is mostly financial news. Multilingual training broadens domain coverage and helps generalization.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

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

## Cited by

Cited by

18 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 13 not yet.

- [The DISRPT 2023 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification](https://md.research.elamur.ai/free/papers/braud-2023-disrpt-2023-shared-task-elementary/)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://md.research.elamur.ai/free/papers/chistova-2024-bilingual-rhetorical-structure-parsing-large/)
- [Improving Multi-Party Dialogue Discourse Parsing via Domain Integration](https://md.research.elamur.ai/free/papers/liu-2021-improving-multi-party-dialogue-discourse/)
- [GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing](https://md.research.elamur.ai/free/papers/peng-2022-gcdt-chinese-rst-treebank-multigenre/)
- [RST Discourse Parsing with Second-Stage EDU-Level Pre-training](https://md.research.elamur.ai/free/papers/yu-2022-rst-discourse-parsing-second-stage/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2020multilingual,
  title = {{Multilingual Neural RST Discourse Parsing}},
  author = {Liu, Zhengyuan and Shi, Ke and Chen, Nancy},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.coling-main.591},
  eprint = {2012.01704},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.coling-main.591}
}
```

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