@inproceedings{gangadharaiah-narayanaswamy-2020-recursive,
    title = "Recursive Template-based Frame Generation for Task Oriented Dialog",
    author = "Gangadharaiah, Rashmi  and
      Narayanaswamy, Balakrishnan",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.acl-main.186/",
    doi = "10.18653/v1/2020.acl-main.186",
    pages = "2059--2064",
    abstract = "The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user{'}s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST). This information is typically represented as a semantic frame that captures the intent and slot-labels provided by the user. We first show that such a shallow representation is insufficient for complex dialog scenarios, because it does not capture the recursive nature inherent in many domains. We propose a recursive, hierarchical frame-based representation and show how to learn it from data. We formulate the frame generation task as a template-based tree decoding task, where the decoder recursively generates a template and then fills slot values into the template. We extend local tree-based loss functions with terms that provide global supervision and show how to optimize them end-to-end. We achieve a small improvement on the widely used ATIS dataset and a much larger improvement on a more complex dataset we describe here."
}