A tree does not make a well-formed sentence: Improving syntactic string-to-tree statistical machine translation with more linguistic knowledge. (July 2015) - Sennrich, Rico; Williams, Philip; Huck, Matthias
Abstract : Highlights: String-to-tree SMT systems aim to produce grammatically well-formed translation. Modelling assumptions of synchronous grammars can result in ungrammatical output. Grammar overgeneralisations can be reduced by parser choice and modifications. Overgeneralisation errors can be prevented with rule-based lingustic constraints. Abstract: Synchronous context-free grammars (SCFGs) can be learned from parallel texts that are annotated with target-side syntax, and can produce translations by building target-side syntactic trees from source strings. Ideally, producing syntactic trees would entail that the translation is grammatically well-formed, but in reality, this is often not the case. Focusing on translation into German, we discuss various ways in which string-to-tree translation models over- or undergeneralise. We show how these problems can be addressed by choosing a suitable parser and modifying its output, by introducing linguistic constraints that enforce morphological agreement and constrain subcategorisation, and by modelling the productive generation of German compounds.