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.
@article{arxiv.1811.00512,
title = {Learning Beam Search Policies via Imitation Learning},
author = {Renato Negrinho and Matthew R. Gormley and Geoffrey J. Gordon},
journal= {arXiv preprint arXiv:1811.00512},
year = {2019}
}