NIPS Proceedingsβ

Learned Prioritization for Trading Off Accuracy and Speed

Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Users want natural language processing (NLP) systems to be both fast and accurate, but quality often comes at the cost of speed. The field has been manually exploring various speed-accuracy tradeoffs (for particular problems and datasets). We aim to explore this space automatically, focusing here on the case of agenda-based syntactic parsing \cite{kay-1986}. Unfortunately, off-the-shelf reinforcement learning techniques fail to learn good policies: the state space is simply too large to explore naively. An attempt to counteract this by applying imitation learning algorithms also fails: the ``teacher'' is far too good to successfully imitate with our inexpensive features. Moreover, it is not specifically tuned for the known reward function. We propose a hybrid reinforcement/apprenticeship learning algorithm that, even with only a few inexpensive features, can automatically learn weights that achieve competitive accuracies at significant improvements in speed over state-of-the-art baselines.