The Regression Model of Machine Translation

Machine translation is the task of automatically nding the translation of a source sentence in the target language. Statistical machine translation (SMT) use parallel corpora or bilingual paired corpora that are known to be translations of each other to nd a likely translation for a given source sentence based on the observed translations. The task of machine translation can be seen as an instance of estimating the functions that map strings to strings. Regression based machine translation (RegMT) approach provides a learning framework for machine translation, separating learning models for training, training instance selection, feature representation, and decoding. We use the transductive learning framework for making the RegMT approach computationally more scalable and consider the model building step independently for each test sentence. We develop training instance selection algorithms that not only make RegMT computationally more scalable but also improve the performance of standard SMT systems. We develop better training instance selection techniques than previous work from given parallel training sentences for achieving more accurate RegMT models using less training instances. We introduce L1 regularized regression as a better model than L2 regularized regression for statistical machine translation. Our results demonstrate that sparse regression models are better than L2 regularized regression for statistical machine translation in predicting target features, estimating word alignments, creating phrase tables, and generating translation outputs. We develop good evaluation techniques for measuring the performance of the RegMT model and the quality of the translations. We use F1 measure, which performs good when evaluating