@article{lowe-etal-2017-training,
    title = "Training End-to-End Dialogue Systems with the {U}buntu Dialogue Corpus",
    author = "Lowe, Ryan  and
      Pow, Nissan  and
      Serban, Iulian Vlad  and
      Charlin, Laurent  and
      Liu, Chia-Wei  and
      Pineau, Joelle",
    editor = "Stent, Amanda  and
      Taboada, Maite  and
      Fern{\'a}ndez, Raquel  and
      Traum, David  and
      Poesio, Massimo  and
      Eugenio, Barbara Di  and
      Stede, Manfred",
    journal = "Dialogue {\&} Discourse",
    volume = "8",
    year = "2017",
    url = "https://preview.aclanthology.org/ingest-dnd/2017.dnd-8.14/",
    doi = "10.5087/dad.2017.102",
    pages = "31--65",
    abstract = "In this paper, we construct and train end-to-end neural network-based dialogue systems usingan updated version of the recent Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu Dialogue Corpus, and for end-to-end dialogue systems in general."
}