[EN]In localisation, there is a constant need to automate processes in an attempt to reduce the cost and time associated with those processes. One of the main ways of achieving this objective is to reuse previously localised data and metadata by using standardised translation memory formats – such as the LISA Translation Memory eXchange (TMX) format or the OASIS XML Localisation Interchange File Format (XLIFF). Although XLIFF has emerged as the most desirable format for the exchange of localisation data, much work still needs to be done before it can be generalised to most localisation activities and can be implemented in a straightforward way. Our research aims to study the importance of the localisation metadata associated with the translation suggestions provided by computer-assisted translation (CAT) tools. We analysed
the ways in which localisation data and metadata (D/M) can be represented in the current specification of XLIFF (i.e. XLIFF 1.2). We designed a new format called the Localisation Memory Container (LMC) to organise previously localised XLIFF files in a single container. Finally, we developed a prototype (XLIFF Phoenix) to leverage the D/M from the LMC to improve the translator’s task by helping CAT tools not only to produce more translation suggestions easily (similar to bi-text approaches), but also to enrich those suggestions with relevant metadata. In order to test whether the ‘CAT-orientated’ enriched metadata are helpful to the translator, we designed an experimental translation task using Swordfish II. Different levels of D/M were introduced in the same document for the different participants, which enabled us to study the influence of the D/M on the translation decision-making process and on the machine–human interaction. In this chapter, we present our research objectives, the methodology and procedures, and an analysis of the results of the experiment. Finally, we extract some preliminary conclusions about the importance of metadata during the localisation process, both as displayed in our experiment and as an integrated function of the tool’s automated capabilities
