Projecting the Knowledge Graph to Syntactic Parsing

Abstract

We present a syntactic parser training paradigm that learns from large scale Knowledge Bases. By utilizing the Knowledge Base context only during training, the resulting parser has no inference-time dependency on the Knowledge Base, thus not decreasing the speed during prediction. Knowledge Base information is injected into the model using an extension to the Augmented-loss training framework. We present empirical results that show this approach achieves a significant gain in accuracy for syntactic categories such as coordination and apposition.