A Study of Imitation Learning Methods for Semantic Role Labeling

Abstract

Global features have proven effective in a wide range of structured prediction problems but come with high inference costs. Imitation learning is a common method for training models when exact inference isn't feasible. We study imitation learning for Semantic Role Labeling (SRL) and analyze the effectiveness of the Violation Fixing Perceptron (VFP) (Huang et al., 2012) and Locally Optimal Learning to Search (LOLS) (Chang et al., 2015) frameworks with respect to SRL global features. We describe problems in applying each framework to SRL and evaluate the effectiveness of some solutions. We also show that action ordering, including easy first inference, has a large impact on the quality of greedy global models.

Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles

“…We utilize majority portions of the Concretely Annotated New York Times and Wikipedia corpora from Ferraro et al (2014). These have been annotated with three frame semantic parses: FrameNet from Das et al (2010), and both FrameNet and PropBank from Wolfe et al (2016). In total, we use nearly five million frame-annotated documents.…”