Paper: A Non-Monotonic Arc-Eager Transition System for Dependency Parsing

ACL ID W13-3518
Title A Non-Monotonic Arc-Eager Transition System for Dependency Parsing
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013
Authors

Previous incremental parsers have used monotonic state transitions. However, transitions can be made to revise previous decisions quite naturally, based on further information. We show that a simple adjustment to the Arc-Eager transition system to relax its monotonicity constraints can improve ac- curacy, so long as the training data in- cludes examples of mistakes for the non- monotonic transitions to repair. We eval- uate the change in the context of a state- of-the-art system, and obtain a statistically significant improvement (p < 0.001) on the English evaluation and 5/10 of the CoNLL languages.