Fuzzy-Match Repair Guided by Quality Estimation
John E. Ortega, Mikel L. Forcada, Felipe Sánchez-Martínez · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020
Computer-aided translation tools based on translation memories are widely used to assist professional translators. A translation memory (TM) consists of a set of translation units (TU) made up of source- and target-language segment pairs. For the translation of a new source segment$s^{\prime }$, these tools search the TM and retrieve the TUs$(s,t)$whose source segments are more similar to$s^{\prime }$. The translator then chooses a TU and edit the target segment$t$to turn it into an adequate translation of$s^{\prime }$.Fuzzy-match repair(FMR) techniques can be used to automatically modify the parts of$t$that need to be edited. We describe a language-independent FMR method that first uses machine translation to generate, given$s^{\prime }$and$(s,t)$, a set of candidate fuzzy-match repaired segments, and then chooses the best one by estimating their quality. An evaluation on three different language pairs shows that the selected candidate is a good approximation to the best (oracle) candidate produced and is closer to reference translations than machine-translated segments and unrepaired fuzzy matches ($t$). In addition, a single quality estimation model trained on a mix of data from all the languages performs well on any of the languages used.