A Quality-based Active Sample Selection Strategy for Statistical Machine Translation
Varvara Logacheva, Lucia Specia · 2014
This paper presents a new active learning technique for machine translation based on quality estimation of automatically translated sentences.It uses an error-driven strategy, i.e., it assumes that the more errors an automatically translated sentence contains, the more informative it is for the translation system.Our approach is based on a quality estimation technique which involves a wider range of features of the source text, automatic translation, and machine translation system compared to previous work.In addition, we enhance the machine translation system training data with post-edited machine translations of the sentences selected, instead of simulating this using previously created reference translations.We found that re-training systems with additional post-edited data yields higher quality translations regardless of the selection strategy used.We relate this to the fact that post-editions tend to be closer to source sentences as compared to references, making the rule extraction process more reliable.