@inproceedings{yu-etal-2017-learning-learn,
    title = "Learning how to Learn: An Adaptive Dialogue Agent for Incrementally Learning Visually Grounded Word Meanings",
    author = "Yu, Yanchao  and
      Eshghi, Arash  and
      Lemon, Oliver",
    editor = "Bansal, Mohit  and
      Matuszek, Cynthia  and
      Andreas, Jacob  and
      Artzi, Yoav  and
      Bisk, Yonatan",
    booktitle = "Proceedings of the First Workshop on Language Grounding for Robotics",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-2802",
    doi = "10.18653/v1/W17-2802",
    pages = "10--19",
    abstract = "We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users, and achieve good learning performance (i.e. accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus {--} a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.",
}