# GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing

arXiv: https://arxiv.org/abs/2210.10449 · HTML: https://mlsanity.com/paper/2210.10449/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (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)
- [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)
- [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)
- [Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA](https://mlsanity.com/paper/2009.08257/) (2020)
- [Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !](https://mlsanity.com/paper/2012.02144/) (2020)
- [Unifying Discourse Resources with Dependency Framework](https://mlsanity.com/paper/2101.00167/) (2021)
- [Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task](https://mlsanity.com/paper/2107.07229/) (2021)
- [Persian Rhetorical Structure Theory](https://mlsanity.com/paper/2106.13833/) (2021)
- [Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees](https://mlsanity.com/paper/1704.05228/) (2017)
- [Zero-shot Chinese Discourse Dependency Parsing via Cross-lingual Mapping](https://mlsanity.com/paper/1911.12014/) (2019)
- [From Sentiment Annotations to Sentiment Prediction through Discourse Augmentation](https://mlsanity.com/paper/2011.03021/) (2020)

## Built on by (most cited first, 14 of 14)

- [The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation](https://mlsanity.com/paper/2211.02570/) (2022)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://mlsanity.com/paper/2302.06488/) (2023)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://mlsanity.com/paper/2403.13560/) (2024)
- [Bilingual Rhetorical Structure Parsing with Large Parallel Annotations](https://mlsanity.com/paper/2409.14969/) (2024)
- [GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains](https://mlsanity.com/paper/2411.00491/) (2024)
- [Are UD Treebanks Getting More Consistent? A Report Card for English UD](https://mlsanity.com/paper/2302.00636/) (2023)
- [Identifying the Periodicity of Information in Natural Language](https://mlsanity.com/paper/2510.27241/) (2025)
- [Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser](https://mlsanity.com/paper/2510.06427/) (2025)
- [A Pilot Study on Dialogue-Level Dependency Parsing for Chinese](https://mlsanity.com/paper/2305.12441/) (2023)
- [DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification](https://mlsanity.com/paper/2509.11498/) (2025)
- [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)
- [Introducing corpora Hlava Cor and Hlava AD: Human Label Variation in Coreference and Discourse Relations](https://mlsanity.com/paper/2606.25383/) (2026)
- [Worth Their Weights? Evaluating Generative Models for Discourse Parsing](https://mlsanity.com/paper/2610.17658/) (2026)

## Abstract (arXiv)

A lack of large-scale human-annotated data has hampered the hierarchical discourse parsing of Chinese. In this paper, we present GCDT, the largest hierarchical discourse treebank for Mandarin Chinese in the framework of Rhetorical Structure Theory (RST). GCDT covers over 60K tokens across five genres of freely available text, using the same relation inventory as contemporary RST treebanks for English. We also report on this dataset's parsing experiments, including state-of-the-art (SOTA) scores for Chinese RST parsing and RST parsing on the English GUM dataset, using cross-lingual training in Chinese and English with multilingual embeddings.