@article{Lowe-Pow-etal-2017.102,
 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.},
 author = {Ryan Lowe and Nissan Pow and Iulian Vlad Serban and Laurent Charlin and Chia-Wei Liu and Joelle Pineau},
 day = {20},
 doi = {10.5087/dad.2017.102},
 issue = {1},
 journal = {Dialogue \& Discourse},
 month = {01},
 pages = {31--65},
 title = {Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus},
 url = {https://journals.uic.edu/ojs/index.php/dad/article/view/10683},
 volume = {8},
 year = {2017}
}
