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.

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In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.

@inproceedings{padmakumar:emnlp18, title={Learning a Policy for Opportunistic Active Learning}, author={Aishwarya Padmakumar and Peter Stone and Raymond J. Mooney}, booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18)}, month={November}, address={Brussels, Belgium}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127713", year={2018} }