Maria Nadejde

Edinburgh’s Syntax-Based Systems at WMT 2014

This paper describes the string-to-tree systems built at the University of Edinburgh for the WMT 2014 shared translation task. We developed systems for English-German, Czech-English, FrenchEnglish, German-English, Hindi-English, and Russian-English. This year we improved our English-German system through target-side compound splitting, morphosyntactic constraints, and refinements to parse tree annotation; we addressed the out-of-vocabulary problem using transliteration for Hindi and Russian and using morphological reduction for Russian; we improved our GermanEnglish system through tree binarization; and we reduced system development time by filtering the tuning sets.

EU-BRIDGE MT: Combined Machine Translation

This paper describes one of the collaborative efforts within EU-BRIDGE to further advance the state of the art in machine translation between two European language pairs, German→English and English→German. Three research institutes involved in the EU-BRIDGE project combined their individual machine translation systems and participated with a joint setup in the shared translation task of the evaluation campaign at the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). We combined up to nine different machine translation engines via system combination. RWTH Aachen University, the University of Edinburgh, and Karlsruhe Institute of Technology developed several individual systems which serve as system combination input. We devoted special attention to building syntax-based systems and combining them with the phrasebased ones. The joint setups yield empirical gains of up to 1.6 points in BLEU and 1.0 points in TER on the WMT newstest2013 test set compared to the best single systems.

Edinburgh's Statistical Machine Translation Systems for WMT16

This paper describes the University of Edinburgh’s phrase-based and syntax-based submissions to the shared translation tasks of the ACL 2016 First Conference on Machine Translation (WMT16). We submitted five phrase-based and five syntaxbased systems for the news task, plus one phrase-based system for the biomedical task.

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.