@inproceedings{tomar-etal-2024-action,
    title = "Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification",
    author = "Tomar, Mohit Singh  and
      Saha, Tulika  and
      Tiwari, Abhisek  and
      Saha, Sriparna",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.28",
    pages = "298--309",
    abstract = "Sarcasm primarily involves saying something but {``}meaning the opposite{''} or {``}meaning something completely different{''} in order to convey a particular tone or mood. In both the above cases, the {``}meaning{''} is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC.",
}

Sarcasm primarily involves saying something but {``}meaning the opposite{''} or {``}meaning something completely different{''} in order to convey a particular tone or mood. In both the above cases, the {``}meaning{''} is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigate a novel phenomenon of analyzing sarcasm in the context of dialogue acts with the hypothesis that the latter helps to understand the former better. Toward this aim, we extend the multi-modal MUStARD dataset to enclose dialogue acts for each dialogue. To demonstrate the utility of our hypothesis, we develop a dialogue act-aided multi-modal transformer network for sarcasm identification (MM-SARDAC), leveraging interrelation between these tasks. In addition, we introduce an order-infused, multi-modal infusion mechanism into our proposed model, which allows for a more intuitive combined modality representation by selectively focusing on relevant modalities in an ordered manner. Extensive empirical results indicate that dialogue act-aided sarcasm identification achieved better performance compared to performing sarcasm identification alone. The dataset and code are available at https://github.com/mohit2b/MM-SARDAC.
Tomar, Mohit Singh Saha, Tulika Tiwari, Abhisek Saha, Sriparna
Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification