Dynamic Feature Selection for Dependency Parsing

Feature computation and exhaustive search have significantly restricted the speed of graph-based dependency parsing.We propose a faster framework of dynamic feature selection, where features are added sequentially as needed, edges are pruned early, and decisions are made online for each sentence.We model this as a sequential decision-making problem and solve it by imitation learning techniques.We test our method on 7 languages.Our dynamic parser can achieve accuracies comparable or even superior to parsers using a full set of features, while computing fewer than 30% of the feature templates.