# What's Hard in English RST Parsing? Predictive Models for Error Analysis

arXiv: https://arxiv.org/abs/2309.04940 · HTML: https://mlsanity.com/paper/2309.04940/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing](https://mlsanity.com/paper/2110.04518/) (2021)
- [GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation](https://mlsanity.com/paper/2306.01966/) (2023)
- [OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres](https://mlsanity.com/paper/2106.00933/) (2021)
- [Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph](https://mlsanity.com/paper/1804.05918/) (2018)
- [A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://mlsanity.com/paper/1905.05682/) (2019)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://mlsanity.com/paper/2302.06488/) (2023)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://mlsanity.com/paper/2304.00815/) (2023)
- [The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error](https://mlsanity.com/paper/2203.11317/) (2022)
- [AMALGUM -- A Free, Balanced, Multilayer English Web Corpus](https://mlsanity.com/paper/2006.10677/) (2020)
- [WikiGUM: Exhaustive Entity Linking for Wikification in 12 Genres](https://mlsanity.com/paper/2109.07449/) (2021)
- [Beyond The Wall Street Journal: Anchoring and Comparing Discourse Signals across Genres](https://mlsanity.com/paper/1909.00516/) (2019)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://mlsanity.com/paper/2204.00350/) (2022)

## Built on by (most cited first, 2 of 2)

- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://mlsanity.com/paper/2403.13560/) (2024)
- [What Causes the Failure of Explicit to Implicit Discourse Relation Recognition?](https://mlsanity.com/paper/2404.00999/) (2024)

## Abstract (arXiv)

Despite recent advances in Natural Language Processing (NLP), hierarchical discourse parsing in the framework of Rhetorical Structure Theory remains challenging, and our understanding of the reasons for this are as yet limited. In this paper, we examine and model some of the factors associated with parsing difficulties in previous work: the existence of implicit discourse relations, challenges in identifying long-distance relations, out-of-vocabulary items, and more. In order to assess the relative importance of these variables, we also release two annotated English test-sets with explicit correct and distracting discourse markers associated with gold standard RST relations. Our results show that as in shallow discourse parsing, the explicit/implicit distinction plays a role, but that long-distance dependencies are the main challenge, while lack of lexical overlap is less of a problem, at least for in-domain parsing. Our final model is able to predict where errors will occur with an accuracy of 76.3% for the bottom-up parser and 76.6% for the top-down parser.