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 Learn...

In this paper we present a novel approach of utilizing Semantic Role Labeling (SRL) information to improve Hierarchical Phrasebased Machine Translation. We propose an algorithm to extract SRL-aware Synchronous Context-Free Grammar (SCFG) rules. Conventional Hiero-style SCFG rules will also be extracted in the same framework. Special conversion rules are applied to ensure that when SRL-aware SCF...

A striking feature of human syntactic processing is that it is context-dependent, that is, it seems to take into account semantic information from the discourse context and world knowledge. In this paper, we attempt to use this insight to bridge the gap between SRL results from gold parses and from automatically-generated parses. To do this, we jointly perform parsing and semantic role labeling...

made significant progress over the last 5 years. We have successfully demonstrated the feasibility of a number of probabilistic models for rela-tional data, including probabilistic relational models, Bayesian logic programs, and relational probability trees, and the interest in SRL is growing. However, in order to sustain and nurture the growth of SRL as a subfield we need to refocus our effort...

The limited instructional support in Massive Open Online Courses (MOOCs) inherently demands learners to self-regulate their learning. MOOC research shows that are more successful when they engage self-regulated learning (SRL) behaviors such as planning what study and reviewing materials. However, many struggle with SRL. In this study, we examined the effect of two types SRL prompts (i.e., quest...

This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear multi-task learning algorithm, which extracts the common structures of multiple tasks to improve accuracy, via the use of auxiliary problems. In this paper, we explore a number of different auxiliary prob...

Nominals frequently surface without overtly expressed arguments. In order to measure the potential benefit of nominal SRL for downstream processes, such nominals must be accounted for. In this paper, we show that a state-of-the-art nominal SRL system with an overall argument F1 of 0.76 suffers a performance loss of more than 9% when nominals with implicit arguments are included in the evaluatio...

Self-regulated learning (SRL) and metacognition are key in the context of 21 century education, adult training, and lifelong learning. For instructional strategies to foster metacognition and self-regulation it is crucial to know what are good metacognitive and SRL behaviors. We investigated this question in the context of a training simulator in a curriculum setting with 152 medical students. ...

Statistical Relational Learning (SRL) combines the benefits of probabilistic machine learning approaches with complex, structured domains from Inductive Logic Programming (ILP). We propose a new SRL algorithm, GleanerSRL, to generate probabilities for highly-skewed relational domains. In this work, we combine clauses from Gleaner, an ILP algorithm for learning a wide variety of first-order clau...