# Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity

arXiv: https://arxiv.org/abs/2302.06488 · HTML: https://mlsanity.com/paper/2302.06488/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 65 references that are in the map.
- First posted on arXiv 13 Feb 2023.

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

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://mlsanity.com/paper/1912.11078/) (2019)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing](https://mlsanity.com/paper/2110.04518/) (2021)
- [SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](https://mlsanity.com/paper/1806.03653/) (2018)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection](https://mlsanity.com/paper/2109.09777/) (2021)
- [Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation](https://mlsanity.com/paper/1906.01569/) (2019)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing](https://mlsanity.com/paper/2210.10449/) (2022)
- [The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error](https://mlsanity.com/paper/2203.11317/) (2022)
- [A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing](https://mlsanity.com/paper/2210.07873/) (2022)
- [AMALGUM -- A Free, Balanced, Multilayer English Web Corpus](https://mlsanity.com/paper/2006.10677/) (2020)
- [Top-down Discourse Parsing via Sequence Labelling](https://mlsanity.com/paper/2102.02080/) (2021)
- [MEGA RST Discourse Treebanks with Structure and Nuclearity from Scalable Distant Sentiment Supervision](https://mlsanity.com/paper/2011.03017/) (2020)
- [Beyond The Wall Street Journal: Anchoring and Comparing Discourse Signals across Genres](https://mlsanity.com/paper/1909.00516/) (2019)
- [Are UD Treebanks Getting More Consistent? A Report Card for English UD](https://mlsanity.com/paper/2302.00636/) (2023)
- [From News to Medical: Cross-domain Discourse Segmentation](https://mlsanity.com/paper/1904.06682/) (2019)

## Built on by (most cited first, 15 of 15)

- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://mlsanity.com/paper/2409.14969/) (2024)
- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains](https://mlsanity.com/paper/2411.00491/) (2024)
- [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)
- [A survey of diversity quantification in natural language processing: The why, what, where and how](https://mlsanity.com/paper/2507.20858/) (2025)
- [Discursive Circuits: How Do Language Models Understand Discourse Relations?](https://mlsanity.com/paper/2510.11210/) (2025)
- [Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser](https://mlsanity.com/paper/2510.06427/) (2025)
- [What's Hard in English RST Parsing? Predictive Models for Error Analysis](https://mlsanity.com/paper/2309.04940/) (2023)
- [We Need to Measure Data Diversity in NLP -- Better and Broader](https://mlsanity.com/paper/2505.20264/) (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)
- [Linguistic and Argument Diversity in Synthetic Data for Function-Calling Agents](https://mlsanity.com/paper/2601.17829/) (2026)
- [Measuring Dataset Diversity from a Geometric Perspective](https://mlsanity.com/paper/2602.09340/) (2026)

## Abstract (arXiv)

Recent advances in discourse parsing performance create the impression that, as in other NLP tasks, performance for high-resource languages such as English is finally becoming reliable. In this paper we demonstrate that this is not the case, and thoroughly investigate the impact of data diversity on RST parsing stability. We show that state-of-the-art architectures trained on the standard English newswire benchmark do not generalize well, even within the news domain. Using the two largest RST corpora of English with text from multiple genres, we quantify the impact of genre diversity in training data for achieving generalization to text types unseen during training. Our results show that a heterogeneous training regime is critical for stable and generalizable models, across parser architectures. We also provide error analyses of model outputs and out-of-domain performance. To our knowledge, this study is the first to fully evaluate cross-corpus RST parsing generalizability on complete trees, examine between-genre degradation within an RST corpus, and investigate the impact of genre diversity in training data composition.