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Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance , but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neu-ral model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transfer-able. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.