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

Maria Nadejde, Alexandra Birch, Philipp Koehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We address the problem of mistranslated predicate-argument structures in syntax based 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.
Original languageEnglish
Title of host publicationProceedings of the First Conference on Machine Translation, Volume 1: Research Papers
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics
Number of pages11
ISBN (Electronic)978-1-945626-10-4
Publication statusPublished - 12 Aug 2016
EventFirst Conference on Machine Translation - Berlin, Germany
Duration: 11 Aug 201612 Aug 2016


ConferenceFirst Conference on Machine Translation
Abbreviated titleWMT16
Internet address


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