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@InProceedings{IJCAI15-thomason,
  author = {Jesse Thomason and Shiqi Zhang and Raymond Mooney and Peter Stone},
  title = {Learning to Interpret Natural Language Commands through Human-Robot
    Dialog},
  booktitle = {Proceedings of the 2015 International Joint Conference on
    Artificial Intelligence (IJCAI)},
  location = {Buenos Aires, Argentina},
  month = {July},
  year = {2015},
  abstract = {
    Intelligent robots frequently need to understand requests from naive users
    through natural language. Previous approaches either cannot account for
    language variation, e.g., keyword search, or require gathering large
    annotated corpora, which can be expensive and cannot adapt to new variation.
    We introduce a dialog agent for mobile robots that understands human
    instructions through semantic parsing, actively resolves ambiguities using a
    dialog manager, and incrementally learns from human-robot conversations by
    inducing training data from user paraphrases. Our dialog agent is
    implemented and tested both on a web interface with hundreds of users via
    Mechanical Turk and on a mobile robot over several days, tasked with
    understanding navigation and delivery requests through natural language in
    an office environment. In both contexts, We observe significant improvements
    in user satisfaction after learning from conversations. 
  },
  wwwnote={<a href="https://youtu.be/FL9IhJQOzb8">Demo</a>},
}