@inproceedings{khanpour-etal-2016-dialogue,
    title = "Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network",
    author = "Khanpour, Hamed  and
      Guntakandla, Nishitha  and
      Nielsen, Rodney",
    editor = "Matsumoto, Yuji  and
      Prasad, Rashmi",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C16-1189/",
    pages = "2012--2021",
    abstract = "In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11{\%}, and MRDA by 2.2{\%}."
}