@InProceedings{miller-EtAl:2017:EMNLP2017Demos,
  author    = {Miller, Alexander  and  Feng, Will  and  Batra, Dhruv  and  Bordes, Antoine  and  Fisch, Adam  and  Lu, Jiasen  and  Parikh, Devi  and  Weston, Jason},
  title     = {ParlAI: A Dialog Research Software Platform},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {79--84},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/D17-2014}
}