Paper: Trait-Based Hypothesis Selection For Machine Translation

ACL ID N12-1059
Title Trait-Based Hypothesis Selection For Machine Translation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

In the area of machine translation (MT) sys- tem combination, previous work on generat- ing input hypotheses has focused on varying a core aspect of the MT system, such as the de- coding algorithm or alignment algorithm. In this paper, we propose a new method for gen- erating diverse hypotheses from a single MT system using traits. These traits are simple properties of the MT output such as ?aver- age output length? and ?average rule length.? Our method is designed to select hypotheses which vary in trait value but do not signif- icantly degrade in BLEU score. These hy- potheses can be combined using standard sys- tem combination techniques to produce a 1.2- 1.5 BLEU gain on the Arabic-English NIST MT06/MT08 translation task.