@inproceedings{huang-etal-2022-dimensional,
    title = "A Dimensional Valence-Arousal-Irony Dataset for {C}hinese Sentence and Context",
    author = "Huang, Sheng-Wei  and
      Chung, Wei-Yi  and
      Wu, Yu-Hsuan  and
      Yu, Chen-Chia  and
      Wu, Jheng-Long",
    booktitle = "Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://aclanthology.org/2022.rocling-1.19",
    pages = "147--154",
    abstract = "Chinese multi-dimensional sentiment detection is a challenging task with a considerable impact on semantic understanding. Past irony datasets are utilized to annotate sentiment type of whole sentences of irony. It does not provide the corresponding intensity of valence and arousal on the sentences and context. However, an ironic statement is defined as a statement whose apparent meaning is the opposite of its actual meaning. This means that in order to understand the actual meaning of a sentence, contextual information is needed. Therefore, the dimensional sentiment intensities of ironic sentences and context are important issues in the natural language processing field. This paper creates the extended NTU irony corpus, which includes valence, arousal and irony intensities on sentence-level; and valence and arousal intensities on context-level, called Chinese Dimensional Valence-Arousal-Irony (CDVAI) dataset. Therefore, this paper analyzes the annotation difference between the human annotators and uses a deep learning model such as BERT to evaluate the prediction performances on CDVAI corpus.",
}