Paper: Graph-based Learning for Statistical Machine Translation

ACL ID N09-1014
Title Graph-based Learning for Statistical Machine Translation
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

Current phrase-based statistical machine translation systems process each test sentence in isolation and do not enforce global consis- tency constraints, even though the test data is often internally consistent with respect to topic or style. We propose a new consistency model for machine translation in the form of a graph-based semi-supervised learning algorithm that exploits similarities between training and test data and also similarities between different test sentences. The algo- rithm learns a regression function jointly over training and test data and uses the resulting scores to rerank translation hypotheses. Eval- uation on two travel expression translation tasks demonstrates improvements of up to 2.6 BLEU points absolute and 2.8% in PER.