@inproceedings{tran-etal-2017-generative,
    title = "A Generative Attentional Neural Network Model for Dialogue Act Classification",
    author = "Tran, Quan Hung  and
      Haffari, Gholamreza  and
      Zukerman, Ingrid",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P17-2083/",
    doi = "10.18653/v1/P17-2083",
    pages = "524--529",
    abstract = "We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations."
}