TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks
Wanqiu Long, Bonnie Webber, Deyi Xiong
Abstract
As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset. TED-CDB comprises a large set of TED talks in Chinese that have been manually annotated according to the goals and principles of Penn Discourse Treebank, but adapted to features that are not present in English. It serves as a unique Chinese corpus of spoken discourse. Benchmark experiments show that TED-CDB poses a challenge for state-of-the-art discourse relation classifiers, whose F1 performance on 4way classification is <60%. This is a dramatic drop of 35% from performance on the news text in the Chinese Discourse Treebank. Transfer learning experiments have been carried out with the TED-CDB for both same-language cross-domain transfer and same-domain crosslanguage transfer. Both demonstrate that the TED-CDB can improve the performance of systems being developed for languages other than Chinese and would be helpful for insufficient or unbalanced data in other corpora. The dataset and our Chinese annotation guidelines has been made freely available. 1
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 77e62ee2-ddb1-4855-9bc3-b2f13d0fc5aeCited by top-tier papers2
- Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense RelationsWanqiu Long, Bonnie WebberEMNLP 2022 · 15 citations
- TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language ModelsJie He, Bo Peng, Yi Liao, Qun Liu et al.ACL 2021
Builds on1
Related papers
- KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven ConversationHao Zhou, Chujie Zheng, Kaili Huang, Minlie Huang et al.ACL 2020 · 106 citations
- Multitask Semi-Supervised Learning for Class-Imbalanced Discourse ClassificationAlexander Spangher, Jonathan May, Sz-Rung Shiang, Lingjia DengEMNLP 2021 · 16 citations
- GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and DomainsYang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu et al.EMNLP 2024
- RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal AnalysisEnzhi Wang, Jiaming Zhou, Yuhang Jia, Aobo Kong et al.ACL 2026
- A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic AnalysisHongyi Wu, Xinshu Shen, Man Lan, Shaoguang Mao et al.EMNLP 2023 · 4 citations
