Recent work in semantic role labeling (SRL) has shown a significant number of semantic roles are expressed outside of the clausal boundary (Ruppenhofer et al., 2010); for instance, the simple sentences: “Joe left for Sydney yesterday. He arrived at 8pm.” requires the reader to infer “Sydney” as the Goal of the “arriving” event, even though it is not explicitly mentioned in the sentence. There is a growing interest in systems that can identify these cases of implicit semantic roles and recover their antecedents (Roth and Frank, 2015). Our work is the first to address the problem of implicit SRL 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 method extends to non-core arguments and does not rely on SRL resources, making it extensible any language with parallel corpora with dependency parses. Josef Ruppenhofer, Russell Lee-Goldman, Caroline Sporleder, and Roser Morante. Beyond sentence-level semantic role labeling: linking argument structures in discourse. Language Resources and Evaluation, November 2012. Michael Roth and Anette Frank. ”Inducing implicit arguments from comparable texts: A framework and its applications.”. Computational Linguistics, 2015.