Here is the dataset of paper TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks. 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. Please note that we checked and updated the corpus once after the publication of the paper, so there is minor change with the number of the relations.
If you use this dataset in your work, please cite the corresponding paper.
The bibtex are listed below:
@inproceedings{long-etal-2020-ted,
title = "{TED}-{CDB}: A Large-Scale {C}hinese Discourse Relation Dataset on {TED} Talks",
author = "Long, Wanqiu and
Webber, Bonnie and
Xiong, Deyi",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.223",
doi = "10.18653/v1/2020.emnlp-main.223",
pages = "2793--2803",
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 4-way 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 cross-language 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 will be made freely available.",
}