Filling in the gaps

Alongside increasing use of Machine Translation (MT) in translator workflows, Translation Memory (TM) continues to be a valuable tool providing complementary functionality, and is a technology that has evolved in recent years, in particular with developments around subsegment recall that attempt to leverage more content from TM data than segment-level fuzzy matching. But how fit-for-purpose is subsegment recall functionality, and how do current ComputerAssisted Translation (CAT) tool implementations differ? This paper presents results from the first survey of translators to gauge their expectations of subsegment recall functionality, crossreferenced with a novel typology for describing subsegment recall implementations. Next, performance statistics are given from an extensive series of tests of four leading CAT tools whose implementations approach those expectations. Finally, a novel implementation of subsegment recall, ‘Lift’, is presented (integrated into SDL Trados Studio 2014), based on subsegment alignment and with no minimum TM size requirement or need for an ‘extraction’ step, recalling fragments and identifying their translations within the segment even with only a single TM occurrence and without losing the context of the match. A technical description explains why it produces better performance statistics for the same series of tests and in turn meets translator expectations more closely.