Abstract

We describe a novel neural network architecture for the problem of semantic role labeling. Many current solutions are complicated, consist of several stages and handbuilt features, and are too slow to be applied as part of real applications that require such semantic labels, partly because of their use of a syntactic parser (Pradhan et al., 2004; Gildea and Jurafsky, 2002). Our method instead learns a direct mapping from source sentence to semantic tags for a given predicate without the aid of a parser or a chunker. Our resulting system obtains accuracies comparable to the current state-of-the-art at a fraction of the computational cost. 1

Citations