Modeling Selectional Preferences of Verbs and Nouns in String-to-Tree Machine Translation

We address the problem of mistranslated predicate-argument structures in syntaxbased machine translation. This paper explores whether knowledge about semantic affinities between the target predicates and their argument fillers is useful for translating ambiguous predicates and arguments. We propose a selectional preference feature based on the selectional association measure of Resnik (1996) and integrate it in a string-to-tree decoder. The feature models selectional preferences of verbs for their core and prepositional arguments as well as selectional preferences of nouns for their prepositional arguments. We compare our features with a variant of the neural relational dependency language model (RDLM) (Sennrich, 2015) and find that neither of the features improves automatic evaluation metrics. We conclude that mistranslated verbs, errors in the target syntactic trees produced by the decoder and underspecified syntactic relations are negatively impacting these features.