# DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing

arXiv: https://arxiv.org/abs/2110.04518 · HTML: https://mlsanity.com/paper/2110.04518/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading](https://mlsanity.com/paper/2010.01838/) (2020)
- [Better Document-level Sentiment Analysis from RST Discourse Parsing](https://mlsanity.com/paper/1509.01599/) (2015)
- [Cross-lingual RST Discourse Parsing](https://mlsanity.com/paper/1701.02946/) (2017)
- [A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://mlsanity.com/paper/1905.05682/) (2019)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [Multilingual Neural RST Discourse Parsing](https://mlsanity.com/paper/2012.01704/) (2020)
- [A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure](https://mlsanity.com/paper/2005.02680/) (2020)
- [Fast Rhetorical Structure Theory Discourse Parsing](https://mlsanity.com/paper/1505.02425/) (2015)
- [An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations](https://mlsanity.com/paper/2012.11169/) (2020)
- [Cross-lingual and cross-domain discourse segmentation of entire documents](https://mlsanity.com/paper/1704.04100/) (2017)

## Built on by (most cited first, 28 of 28)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [SiReRAG: Indexing Similar and Related Information for Multihop Reasoning](https://mlsanity.com/paper/2412.06206/) (2024)
- [Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs](https://mlsanity.com/paper/2402.10586/) (2024)
- [Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization](https://mlsanity.com/paper/2305.16784/) (2023)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://mlsanity.com/paper/2302.06488/) (2023)
- [GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing](https://mlsanity.com/paper/2210.10449/) (2022)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://mlsanity.com/paper/2403.13560/) (2024)
- [RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization](https://mlsanity.com/paper/2405.00657/) (2024)
- [Generating EDU Extracts for Plan-Guided Summary Re-Ranking](https://mlsanity.com/paper/2305.17779/) (2023)
- [Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization](https://mlsanity.com/paper/2502.06185/) (2025)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://mlsanity.com/paper/2409.14969/) (2024)
- [Align to Structure: Aligning Large Language Models with Structural Information](https://mlsanity.com/paper/2504.03622/) (2025)
- [Can we obtain significant success in RST discourse parsing by using Large Language Models?](https://mlsanity.com/paper/2403.05065/) (2024)
- [Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set](https://mlsanity.com/paper/2503.10515/) (2025)
- [Explanatory Summarization with Discourse-Driven Planning](https://mlsanity.com/paper/2504.19339/) (2025)
- [Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method](https://mlsanity.com/paper/2402.06125/) (2024)
- [Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser](https://mlsanity.com/paper/2510.06427/) (2025)
- [Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection](https://mlsanity.com/paper/2604.04932/) (2026)
- [What's Hard in English RST Parsing? Predictive Models for Error Analysis](https://mlsanity.com/paper/2309.04940/) (2023)
- [Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction](https://mlsanity.com/paper/2504.12324/) (2025)
- [End-to-End Argument Mining over Varying Rhetorical Structures](https://mlsanity.com/paper/2401.11218/) (2024)
- [Synthetic Data Augmentation for Cross-domain Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.20588/) (2025)
- [A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization](https://mlsanity.com/paper/2504.16711/) (2025)
- [CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations](https://mlsanity.com/paper/2506.08504/) (2025)
- [Document Understanding, Measurement, and Manipulation Using Category Theory](https://mlsanity.com/paper/2510.21553/) (2025)
- [D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection](https://mlsanity.com/paper/2608.27380/) (2026)
- [Does Linguistic Structure Enrichment Enhance Coherence Assessment? Not With Current Architectures](https://mlsanity.com/paper/2609.10893/) (2026)
- [Worth Their Weights? Evaluating Generative Models for Discourse Parsing](https://mlsanity.com/paper/2610.17658/) (2026)

## Abstract (arXiv)

Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data. (2) Most parsers cannot be used in multilingual scenarios, because they are developed only in English. (3) Parsers trained from single-domain treebanks do not generalize well on out-of-domain inputs. In this work, we propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Moreover, we propose a cross-translation augmentation strategy to enable the framework to support multilingual parsing and improve its domain generality. Experimental results show that our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks.