Paper: Training a Log-Linear Parser with Loss Functions via Softmax-Margin

ACL ID D11-1031
Title Training a Log-Linear Parser with Loss Functions via Softmax-Margin
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

Log-linear parsing models are often trained by optimizing likelihood, but we would prefer to optimise for a task-specific metric like F- measure. Softmax-margin is a convex objec- tive for such models that minimises a bound on expected risk for a given loss function, but its na¨ıve application requires the loss to de- compose over the predicted structure, which is not true of F-measure. We use softmax- margin to optimise a log-linear CCG parser for a variety of loss functions, and demonstrate a novel dynamic programming algorithm that enables us to use it with F-measure, lead- ing to substantial gains in accuracy on CCG- Bank. When we embed our loss-trained parser into a larger model that includes supertagging features incorporated via belief propagation, we obtain further improvements a...