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MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models

  • Published:
  • Version: June 2019
  • Category: other
  • Language: English

MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models

We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total. Dialogues are a minimum of 10 turns long

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Operating Systems: Windows 10, Windows XP, Windows 7, Windows 8

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