Michael Jahr

Fast Decoding and Optimal Decoding for Machine Translation

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoders job is to find the translation that is most likely according to set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.

Fast and optimal decoding for machine translation

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoders job is to find the translation that is most likely according to a set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. Unfortunately, examining more of the space leads to unacceptably slow decodings.In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast but non-optimal greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.

Translation-Inspired OCR

Optical character recognition is carried out using techniques borrowed from statistical machine translation. In particular, the use of multiple simple feature functions in linear combination, along with minimum-error-rate training, integrated decoding, and

Statistical Machine Translation: Final Report

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Cairo: An Alignment Visualization Tool

-gram language modeling is found to be remarkably effective, across several scripts and languages. Results are presented using both synthetic and real data in five languages.