@inproceedings{tanaka-etal-2019-dialogue,
    title = "Dialogue-Act Prediction of Future Responses Based on Conversation History",
    author = "Tanaka, Koji  and
      Takayama, Junya  and
      Arase, Yuki",
    editor = "Alva-Manchego, Fernando  and
      Choi, Eunsol  and
      Khashabi, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/P19-2027/",
    doi = "10.18653/v1/P19-2027",
    pages = "197--202",
    abstract = "Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8{\%} higher F1-score and 3.0{\%} higher accuracy on DA prediction."
}