{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parlai-a-dialog-research-software-platform","title":"ParlAI: A Dialog Research Software Platform","arxiv_id":"1705.06476","date":"2017-05-18","proceeding":"EMNLP 2017 9","authors":["Alexander H. Miller","Will Feng","Adam Fisch","Jiasen Lu","Dhruv Batra","Antoine Bordes","Devi Parikh","Jason Weston"],"abstract":"We introduce ParlAI (pronounced \"par-lay\"), an open-source software platform\nfor dialog research implemented in Python, available at http://parl.ai. Its\ngoal is to provide a unified framework for sharing, training and testing of\ndialog models, integration of Amazon Mechanical Turk for data collection, human\nevaluation, and online/reinforcement learning; and a repository of machine\nlearning models for comparing with others' models, and improving upon existing\narchitectures. Over 20 tasks are supported in the first release, including\npopular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail,\nCBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated,\nincluding neural models such as memory networks, seq2seq and attentive LSTMs.","url_abs":"http://arxiv.org/abs/1705.06476v4","url_pdf":"http://arxiv.org/pdf/1705.06476v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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