@inproceedings{henderson-etal-2019-repository,
    title = "A Repository of Conversational Datasets",
    author = "Henderson, Matthew  and
      Budzianowski, Pawe{\l}  and
      Casanueva, I{\~n}igo  and
      Coope, Sam  and
      Gerz, Daniela  and
      Kumar, Girish  and
      Mrk{\v{s}}i{\'c}, Nikola  and
      Spithourakis, Georgios  and
      Su, Pei-Hao  and
      Vuli{\'c}, Ivan  and
      Wen, Tsung-Hsien",
    editor = "Chen, Yun-Nung  and
      Bedrax-Weiss, Tania  and
      Hakkani-Tur, Dilek  and
      Kumar, Anuj  and
      Lewis, Mike  and
      Luong, Thang-Minh  and
      Su, Pei-Hao  and
      Wen, Tsung-Hsien",
    booktitle = "Proceedings of the First Workshop on NLP for Conversational AI",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-4101",
    doi = "10.18653/v1/W19-4101",
    pages = "1--10",
    abstract = "Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using 1-of-100 accuracy. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.",
}