Feature Weight Optimization for Discourse-Level SMT
2013 (English)In: Proceedings of the Workshop on Discourse in Machine Translation (DiscoMT), Association for Computational Linguistics, 2013, 60-69 p.Conference paper (Refereed)
We present an approach to feature weight optimization for document-level decoding. This is an essential task for enabling future development of discourse-level statistical machine translation, as it allows easy integration of discourse features in the decoding process. We extend the framework of sentence-level feature weight optimization to the document-level. We show experimentally that we can get competitive and relatively stable results when using a standard set of features, and that this framework also allows us to optimize document- level features, which can be used to model discourse phenomena.
Place, publisher, year, edition, pages
Association for Computational Linguistics, 2013. 60-69 p.
SMT, Cross-sentence SMT, Feature weight optimisation
Language Technology (Computational Linguistics)
Research subject Computational Linguistics
IdentifiersURN: urn:nbn:se:uu:diva-207764OAI: oai:DiVA.org:uu-207764DiVA: diva2:649446
DiscoMT (Discourse in Machine Translation) 2013; 9 August 2013; Sofia, Bulgaria