ParlAI: A Dialog Research Software Platform

Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, Jason Weston


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.
Anthology ID:
D17-2014
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–84
Language:
URL:
https://www.aclweb.org/anthology/D17-2014
DOI:
10.18653/v1/D17-2014
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-2014.pdf