CEDCC_corpus

add datasets and baselines

EMNLP 2023 paper

A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis(https://aclanthology.org/2023.emnlp-main.412/)

Citation

@inproceedings{wu-etal-2023-multi-task,
    title = "A Multi-Task Dataset for Assessing Discourse Coherence in {C}hinese Essays: Structure, Theme, and Logic Analysis",
    author = "Wu, Hongyi  and
      Shen, Xinshu  and
      Lan, Man  and
      Mao, Shaoguang  and
      Bai, Xiaopeng  and
      Wu, Yuanbin",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.412",
    doi = "10.18653/v1/2023.emnlp-main.412",
    pages = "6673--6688",
    abstract = "This paper introduces the \textbf{C}hinese \textbf{E}ssay \textbf{D}iscourse \textbf{C}oherence \textbf{C}orpus (\textbf{CEDCC}), a multi-task dataset for assessing discourse coherence. Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations. This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis. Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring. The dataset and related codes is available at \url{https://github.com/cubenlp/CEDCC_corpus}.",
}