# Top-down Discourse Parsing via Sequence Labelling

arXiv: https://arxiv.org/abs/2102.02080 · HTML: https://mlsanity.com/paper/2102.02080/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Mining Discourse Markers for Unsupervised Sentence Representation Learning](https://mlsanity.com/paper/1903.11850/) (2019)
- [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)
- [A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure](https://mlsanity.com/paper/2005.02680/) (2020)
- [Hierarchical Pointer Net Parsing](https://mlsanity.com/paper/1908.11571/) (2019)
- [Improved Document Modelling with a Neural Discourse Parser](https://mlsanity.com/paper/1911.06919/) (2019)

## Built on by (most cited first, 9 of 9)

- [Discourse Probing of Pretrained Language Models](https://mlsanity.com/paper/2104.05882/) (2021)
- [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)
- [Towards Understanding Large-Scale Discourse Structures in Pre-Trained and Fine-Tuned Language Models](https://mlsanity.com/paper/2204.04289/) (2022)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://mlsanity.com/paper/2409.14969/) (2024)
- [A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing](https://mlsanity.com/paper/2210.08355/) (2022)
- [Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsing](https://mlsanity.com/paper/2210.09537/) (2022)
- [RST-style Discourse Parsing Guided by Document-level Content Structures](https://mlsanity.com/paper/2309.04141/) (2023)
- [DLUE: Benchmarking Document Language Understanding](https://mlsanity.com/paper/2305.09520/) (2023)

## Abstract (arXiv)

We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points. We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing.