@inproceedings{miller-etal-2017-parlai,
title = "{P}arl{AI}: A Dialog Research Software Platform",
author = "Miller, Alexander and
Feng, Will and
Batra, Dhruv and
Bordes, Antoine and
Fisch, Adam and
Lu, Jiasen and
Parikh, Devi and
Weston, Jason",
editor = "Specia, Lucia and
Post, Matt and
Paul, Michael",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-2014",
doi = "10.18653/v1/D17-2014",
pages = "79--84",
abstract = "We introduce ParlAI (pronounced {``}par-lay{''}), an open-source software platform for dialog research implemented in Python, available at \url{http://parl.ai}. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others{'} models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.",
}
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%0 Conference Proceedings
%T ParlAI: A Dialog Research Software Platform
%A Miller, Alexander
%A Feng, Will
%A Batra, Dhruv
%A Bordes, Antoine
%A Fisch, Adam
%A Lu, Jiasen
%A Parikh, Devi
%A Weston, Jason
%Y Specia, Lucia
%Y Post, Matt
%Y Paul, Michael
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F miller-etal-2017-parlai
%X We introduce ParlAI (pronounced “par-lay”), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing dialog models; integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others’ models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.
%R 10.18653/v1/D17-2014
%U https://aclanthology.org/D17-2014
%U https://doi.org/10.18653/v1/D17-2014
%P 79-84
Markdown (Informal)
[ParlAI: A Dialog Research Software Platform](https://aclanthology.org/D17-2014) (Miller et al., EMNLP 2017)
ACL
- Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. ParlAI: A Dialog Research Software Platform. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 79–84, Copenhagen, Denmark. Association for Computational Linguistics.