Choosing the Right Translation: A Syntactically Informed Classification Approach

One style of Multi-Engine Machine Translation architecture involves choosing the best of a set of outputs from different systems. Choosing the best translation from an arbitrary set, even in the presence of human references, is a difficult problem; it may prove better to look at mechanisms for making such choices in more restricted contexts. In this paper we take a classificationbased approach to choosing between candidates from syntactically informed translations. The idea is that using multiple parsers as part of a classifier could help detect syntactic problems in this context that lead to bad translations; these problems could be detected on either the source side--perhaps sentences with difficult or incorrect parses could lead to bad translations--or on the target side--perhaps the output quality could be measured in a more syntactically informed way, looking for syntactic abnormalities. We show that there is no evidence that the source side information is useful. However, a targ...

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Simon Zwarts, Mark Dras
• Keywords: Choosing, COLING 2008, Computational Linguistics, Difficult Problem, Multi-Engine Machine Translation