6 research outputs found
Learning a Policy for Opportunistic Active Learning
Active learning identifies data points to label that are expected to be the
most useful in improving a supervised model. Opportunistic active learning
incorporates active learning into interactive tasks that constrain possible
queries during interactions. Prior work has shown that opportunistic active
learning can be used to improve grounding of natural language descriptions in
an interactive object retrieval task. In this work, we use reinforcement
learning for such an object retrieval task, to learn a policy that effectively
trades off task completion with model improvement that would benefit future
tasks.Comment: EMNLP 2018 Camera Read
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Dialog as a vehicle for lifelong learning of grounded language understanding systems
Natural language interfaces have the potential to make various forms of technology, including mobile phones and computers as well as robots or other machines such as ATMs and self-checkout counters, more accessible and less intimidating to users who are unfamiliar or uncomfortable with other types of interfaces. In particular, natural language understanding systems on physical robots face a number of challenges, including the need to ground language in perception, the ability to adapt to changes in the environment and novel uses of language, and to deal with uncertainty in understanding. To effectively handle these challenges, such systems need to perform lifelong learning - continually updating the scope and predictions of the model with user interactions. In this thesis, we discuss ways in which dialog interaction with users can be used to improve grounded natural language understanding systems, motivated by service robot applications.
We focus on two types of queries that can be used in such dialog systems – active learning queries to elicit knowledge about the environment that can be used to improve perceptual models, and clarification questions that confirm the system’s hypotheses, or elicit specific information required to complete a task. Our goal is to build a system that can learn how to interact with users balancing a quick completion of tasks desired by the user with asking additional active learning questions viiito improve the underlying grounded language understanding components.
We present work on jointly improving semantic parsers from and learning a dialog policy for clarification dialogs, that improve a robot’s ability to understand natural language commands. We introduce the framework of opportunistic active learning, where a robot introduces opportunistic queries, that may not be immediately relevant, into an interaction in the hope of improving performance in future interactions. We demonstrate the usefulness of this framework in learning to ground natural language descriptions of objects, and learn a dialog policy for such interactions. We also learn dialog policies that balance task completion, opportunistic active learning, and attribute-based clarification questions. Finally, we attempt to expand this framework to different types of underlying models of grounded language understanding.Computer Science