Jessie Pinkham

Adding domain specificity to an MT system

In the development of a machine translation system, one important issue is being able to adapt to a specific domain without requiring time-consuming lexical work. We have experimented with using a statistical word-alignment algorithm to derive word association pairs (French-English) that complement an existing multi-purpose bilingual dictionary. This word association information is added to the system at the time of the automatic creation of our translation pattern database, thereby making this database more domain specific. This technique significantly improves the overall quality of translation, as measured in an independent blind evaluation.

MSR-MT: The Microsoft Research Machine Translation System

MSR-MT is an advanced research MT prototype that combines rulebased and statistical techniques with example-based transfer. This hybrid, large-scale system is capable of learning all its knowledge of lexical and phrasal translations directly from data. MSR-MT has undergone rigorous evaluation showing that, trained on a corpus of technical data similar to the test corpus, its output surpasses the quality of best-of-breed commercial MT systems.

Modular MT with a learned bilingual dictionary: rapid deployment of a new language pair

The MT system described in this paper combines hand-built analysis and generation components with automatically learned example-based transfer patterns. Up to now, the transfer component used a traditional bilingual dictionary to seed the transfer pattern learning process and to provide fallback translations at runtime. This paper describes an improvement to the system by which the bilingual dictionary used for these purposes is instead learned automatically from aligned bilingual corpora, making the systems transfer knowledge entirely derivable from corpora. We show that this system with a fully automated transfer process performs better than the system with a hand-crafted bilingual dictionary. More importantly, this has enabled us to create in less than one day a new language pair, French-Spanish, which, for a technical domain, surpasses the quality bar of the commercial system chosen for comparison.

Machine translation as a testbed for multilingual analysis

We propose that machine translation (MT) is a useful application for evaluating and deriving the development of NL components, especially in a wide-coverage analysis system. Given the architecture of our MT system, which is a transfer system based on linguistic modules, correct analysis is expected to be a prerequisite for correct translation, suggesting a correlation between the two, given relatively mature transfer and generation components. We show through error analysis that there is indeed a strong correlation between the quality of the translated output and the subjectively determined goodness of the analysis. We use this correlation as a guide for development of a coordinated parallel analysis effort in 7 languages.