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 supervised parsing objective function with additional loss-functions, either based on intrinsic parsing quality or task-specific extrinsic measures of quality. Our empirical results show how this approach performs for two dependency 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.
REF investigates joint optimization of a supervised parsing objective and some extrinsic objectives based on downstream applications.
Training dependency parsers by jointly optimizing multiple objectives
EMNLP
Computer Science