TY - GEN
T1 - Feature Weight Optimization for Discourse-Level SMT
AU - Stymne, Sara
AU - Hardmeier, Christian
AU - Tiedemann, Jörg
AU - Nivre, Joakim
PY - 2013/8/31
Y1 - 2013/8/31
N2 - 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.
AB - 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.
KW - Statistical machine translation
KW - Document-level decoding
KW - Feature weight optimization
KW - Discourse features
KW - Discourse phenomena
M3 - Article in proceedings
BT - Proceedings of the Workshop on Discourse in Machine Translation
ER -