Generalized Higher-Order Dependency Parsing with Cube Pruning

Hao Zhang, Ryan T. McDonald

Abstract: State-of-the-art graph-based parsers use features over higher-order dependencies that rely on decoding algorithms that are slow and difficult to generalize. On the other hand, transition-based dependency parsers can easily utilize such features without increasing the linear complexity of the shift-reduce system beyond a constant. In this paper, we attempt to address this imbalance for graph-based parsing by generalizing the Eisner (1996) algorithm to handle arbitrary features over higher-order dependencies. The generalization is at the cost of asymptotic efficiency. To account for this, cube pruning for decoding is utilized (Chiang, 2007). For the first time, label tuple and structural features such as valencies can be scored efficiently with third-order features in a graph-based parser. Our parser achieves the state-of-art unlabeled accuracy of 93.06% and labeled accuracy of 91.86% on the standard test set for English, at a faster speed than a reimplementation of the third-order model of Koo et al. (2010).