# Unifying Discourse Resources with Dependency Framework

arXiv: https://arxiv.org/abs/2101.00167 · HTML: https://mlsanity.com/paper/2101.00167/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 1 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](https://mlsanity.com/paper/1806.03653/) (2018)
- [Implicit Discourse Relation Classification via Multi-Task Neural Networks](https://mlsanity.com/paper/1603.02776/) (2016)
- [Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping](https://mlsanity.com/paper/1911.12014/) (2019)
- [Chinese Discourse Segmentation Using Bilingual Discourse Commonality](https://mlsanity.com/paper/1809.01497/) (2018)

## Built on by (most cited first, 8 of 8)

- [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)
- [Towards Unification of Discourse Annotation Frameworks](https://mlsanity.com/paper/2204.07781/) (2022)
- [Improve Discourse Dependency Parsing with Contextualized Representations](https://mlsanity.com/paper/2205.02090/) (2022)
- [DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification](https://mlsanity.com/paper/2509.11498/) (2025)
- [A Novel Dependency Framework for Enhancing Discourse Data Analysis](https://mlsanity.com/paper/2407.12473/) (2024)
- [CLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification](https://mlsanity.com/paper/2509.16903/) (2025)
- [DiscoExplorer: An Open Interface for the Study of Multilingual Discourse Relations](https://mlsanity.com/paper/2605.15304/) (2026)

## Abstract (arXiv)

For text-level discourse analysis, there are various discourse schemes but relatively few labeled data, because discourse research is still immature and it is labor-intensive to annotate the inner logic of a text. In this paper, we attempt to unify multiple Chinese discourse corpora under different annotation schemes with discourse dependency framework by designing semi-automatic methods to convert them into dependency structures. We also implement several benchmark dependency parsers and research on how they can leverage the unified data to improve performance.