Paper: What a Parser Can Learn from a Semantic Role Labeler and Vice Versa

ACL ID D10-1072
Title What a Parser Can Learn from a Semantic Role Labeler and Vice Versa
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a sys- tem that allows information to flow in both direc- tions. We make use of semantic role predictions in choosing a single-best parse. This process relies on an averaged perceptron model to distinguish likely semantic roles from erroneous ones. Our system pe- nalizes parses that give rise to low-scoring semantic roles. To explore the consequences of this we per- form two experiments. First, we use a baseline gen- erative model to produce n-best parses, which are then re-ordered by our semantic model. Second, we use a modified version of our semantic role labeler to predict semantic roles at parse time. The perfor- mance of this modified...