Training a Log-Linear Parser with Loss Functions via Softmax-Margin
Year: 2011
Type: article
Abstract: 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 objective for such models that minimises a bound on expected risk for a given loss function, but its naïve application requires the loss to decompose over the predicted structure, which is not true of F-measure. We use softmaxmargin 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, leading 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 and achieve a labelled/unlabelled dependency F-measure of 89.3%/94.0 % on gold part-of-speech tags, and 87.2%/92.8 % on automatic part-of-speech tags, the best reported results for this task. 1
Authors: Michael Auli, Adam Lopez
Institutions: University of Edinburgh, Johns Hopkins University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2250993494