Paper: Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees

ACL ID P14-1019
Title Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Much of the recent work on depen- dency parsing has been focused on solv- ing inherent combinatorial problems as- sociated with rich scoring functions. In contrast, we demonstrate that highly ex- pressive scoring functions can be used with substantially simpler inference pro- cedures. Specifically, we introduce a sampling-based parser that can easily han- dle arbitrary global features. Inspired by SampleRank, we learn to take guided stochastic steps towards a high scoring parse. We introduce two samplers for traversing the space of trees, Gibbs and Metropolis-Hastings with Random Walk. The model outperforms state-of-the-art re- sults when evaluated on 14 languages of non-projective CoNLL datasets. Our sampling-based approach naturally ex- tends to joint prediction scenarios, such as joint ...