Learning Beam Search Policies via Imitation Learning

Renato Negrinho; Matthew R. Gormley; Geoffrey J. Gordon

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arXiv · 1811.00512

Learning Beam Search Policies via Imitation Learning

Renato Negrinho·Matthew R. Gormley·Geoffrey J. Gordon

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Abstract

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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BibTeX RIS

Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon. 2018-11-01. Learning Beam Search Policies via Imitation Learning. https://arxiv.org/abs/1811.00512

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