Open Access

Modern theoretical linguistics lives by the insight that the meanings of complex expressions derive from the meanings of their parts and the way these are composed. However, the currently dominating theories of the syntax-semantics interface hastily relegate important aspects of meaning which cannot readily be aligned with visible structure to empty projecting heads non-reductively (mainstream Generative Grammar) or to the syntactic construction holistically (Construction Grammar). This book develops an alternative, compositional analysis of the hidden aspectual-temporal, modal and comparative meanings of a range of productive constructions of which pseudorefl exive, excessive and directional complement constructions take center stage. Accordingly, a contradiction-inducing hence semantically problematic part of literally coded meaning is locally ignored and systematically realized „expatriately“ with respect to parts of structure that achieve the indexical anchoring of propositional contents in terms of times, worlds and standards of comparison, thus yielding the observed hidden meanings.

We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependencybased and span-based SRL annotations. We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets. We then train our model in a cross-lingual setting to generate new SRL labeled data. Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations. Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.