@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."
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%0 Conference Proceedings
%T A Repository of Conversational Datasets
%A Henderson, Matthew
%A Budzianowski, Paweł
%A Casanueva, Iñigo
%A Coope, Sam
%A Gerz, Daniela
%A Kumar, Girish
%A Mrkšić, Nikola
%A Spithourakis, Georgios
%A Su, Pei-Hao
%A Vulić, Ivan
%A Wen, Tsung-Hsien
%Y Chen, Yun-Nung
%Y Bedrax-Weiss, Tania
%Y Hakkani-Tur, Dilek
%Y Kumar, Anuj
%Y Lewis, Mike
%Y Luong, Thang-Minh
%Y Su, Pei-Hao
%Y Wen, Tsung-Hsien
%S Proceedings of the First Workshop on NLP for Conversational AI
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F henderson-etal-2019-repository
%X 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.
%R 10.18653/v1/W19-4101
%U https://aclanthology.org/W19-4101/
%U https://doi.org/10.18653/v1/W19-4101
%P 1-10
Markdown (Informal)
[A Repository of Conversational Datasets](https://aclanthology.org/W19-4101/) (Henderson et al., ACL 2019)
ACL
- Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulić, and Tsung-Hsien Wen. 2019. A Repository of Conversational Datasets. In Proceedings of the First Workshop on NLP for Conversational AI, pages 1–10, Florence, Italy. Association for Computational Linguistics.