Poster
Learning Beam Search Policies via Imitation Learning
Renato Negrinho · Matthew Gormley · Geoffrey Gordon
Room 517 AB #104
Keywords: [ Structured Prediction ] [ Online Learning ] [ Learning Theory ]
Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do not explicitly learn how to use the beam. We develop an unifying meta-algorithm for learning beam search policies using imitation learning. In our setting, the beam is part of the model and not just an artifact of approximate decoding. Our meta-algorithm captures existing learning algorithms and suggests new ones. It also lets us show novel no-regret guarantees for learning beam search policies.
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