NeurIPS 2018poster33 citations
Learning Beam Search Policies via Imitation Learning
Renato Negrinho, Matthew Gormley, Geoffrey J. Gordon
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.
BibTeX
@inproceedings{NEURIPS2018_967c2ae0,
author = {Negrinho, Renato and Gormley, Matthew and Gordon, Geoffrey J},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Learning Beam Search Policies via Imitation Learning},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/967c2ae04b169f07e7fa8fdfd110551e-Paper.pdf},
volume = {31},
year = {2018}
}