Toward Multi-Engine Machine Translation

Sergei Nirenburg, Robert E. Frederking

Abstract: Current MT systems, whatever translation method they at present employ, do not reach an optimum output on free text. Our hypothesis for the experiment reported in this paper is that if an MT environment can use the best results from a variety of MT systems working simultaneously on the same text, the overall quality will improve. Using this novel approach to MT in the latest version of the Pangloss MT project, we submit an input text to a battery of machine translation systems (engines), collect their (possibly, incomplete) results in a joint chart-like data structure and select the overall best translation using a set of simple heuristics. This paper describes the simple mechanism we use for combining the findings of the various translation engines.