Paper: Choosing the Right Translation: A Syntactically Informed Classification Approach

ACL ID C08-1145
Title Choosing the Right Translation: A Syntactically Informed Classification Approach
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

One style of Multi-Engine Machine Translation architecture involves choos- ing 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 classification- based 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 transla- tions; these problems could be detected on either the source side—perhaps sen- tences with difficult or incorrect parses could lead to bad translations—or on the target side—per...