Фразалық модельдер

State of the art in statistical machine trans- lation is currently represented by phrase- based models, which typically incorpo- rate a large number of probabilities of phrase-pairs and word n-grams. …...

A major engineering challenge in statistical machine translation systems is the efficient representation of extremely large translation rulesets. In phrase-based models, this prob- lem can be addressed by storing the …...

AbstractThe RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase …...

Statistical machine translation systems have greatly improved in the last years. However, this boost in performance usu- ally comes at a high computational cost, yielding systems that are often not …...

Hiero translation models have two lim- itations compared to phrase-based mod- els: 1) Limited hypothesis space; 2) No lexicalized reordering model. We pro- pose an extension of Hiero called Phrasal- …...

We investigate the use of generalized representations (POS, morphological analysis and word clusters) in phrase-based models and the N-gram-based Operation Sequence Model (OSM). Our integration enables these models to learn …...

Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors …...

AbstractTraining neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrase-based models. Policy gradient training enables direct optimization for …...