Implicit Semantic Roles in a Multilingual Setting

Abstract

Extending semantic role labeling (SRL) to detect and recover non-local arguments continues to be a challenge. Our work is the first to address the detection of implicit roles from a multilingual perspective. We map predicate-argument structures across English and German sentences, and we develop a classifier that distinguishes implicit arguments from other translation shifts. Using a combination of alignment statistics and linguistic features, we achieve a precision of 0.68 despite a limited training set, which is a significant gain over the majority baseline. Our approach does not rely on pre-existing knowledge bases and is extendible to any language pair with parallel data and dependency parses.