Fuzzy-Match Repair Guided by Quality Estimation

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^', these tools search the TM and retrieve the TUs (s,t) whose source segments are more similar to s^'. The translator then chooses a TU and edit the target segment t to turn it into an adequate translation of s^'. 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^' 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.