Paper: Training dependency parsers by jointly optimizing multiple objectives

ACL ID D11-1138
Title Training dependency parsers by jointly optimizing multiple objectives
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard su- pervised parsing objective function with addi- tional loss-functions, either based on intrinsic parsing quality or task-specific extrinsic mea- sures of quality. Our empirical results show how this approach performs for two depen- dency parsing algorithms (graph-based and transition-based parsing) and how it achieves increased performance on multiple target tasks including reordering for machine translation and parser adaptation.