@inproceedings{kim-etal-2021-neuralwoz,
    title = "{N}eural{WOZ}: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation",
    author = "Kim, Sungdong  and
      Chang, Minsuk  and
      Lee, Sang-Woo",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.acl-long.287/",
    doi = "10.18653/v1/2021.acl-long.287",
    pages = "3704--3717",
    abstract = "We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generates dialogues from (1) user{'}s goal instructions, which are the user context and task constraints in natural language, and (2) system{'}s API call results, which is a list of possible query responses for user requests from the given knowledge base. Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results. We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking. In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-the-art with improvements of 4.4{\%} point joint goal accuracy on average across domains, and improvements of 5.7{\%} point of zero-shot coverage against the MultiWOZ 2.1 dataset."
}