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@inproceedings{EMNLP18-padmakumar,
title={Learning a Policy for Opportunistic Active Learning},
author={Aishwarya Padmakumar and Peter Stone and Raymond J. Mooney},
booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-18)},
month={November},
address={Brussels, Belgium},
url="http://www.cs.utexas.edu/users/ml/papers/padmakumar.emnlp18.pdf",
year={2018},
abstract={
      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. 
},
location={Brussels, Belgium}
}