We describe a case study that presents a framework for examining whether Machine Translation (MT) output enables translation professionals to translate faster while at the same time producing better quality translations than without MT output. We seek to find decision factors that enable a translation professional, known as a Paralinguist, to determine whether MT output is of sufficient quality to serve as a “seed translation” for post-editors. The decision factors, unlike MT developers’ automatic metrics, must function without a reference translation. We also examine the correlation of MT developers’ automatic metrics with error annotators’ assessments of post-edited translations.

In this paper, we describe the methods used to develop an exchangeable translation memory bank of sentence-aligned Mandarin Chinese - English sentences. This effort is part of a larger effort, initiated by the National Virtual Translation Center (NVTC), to foster collaboration and sharing of translation memory banks across the Intelligence Community and the Department of Defense. In this paper, we describe our corpus creation process - a largely automated process - highlighting the human interventions that are still deemed necessary. We conclude with a brief discussion of how this work will affect plans for NVTC's new translation management workflow and future research to increase the performance of the automated components of the corpus creation process.

This paper describes the strategic vision for a new translation management workflow for the US Government’s National Virtual Translation Center (NVTC). The paper also describes past, current, and planned experiments validating the vision, along with experiment results to-date. The most salient features of the new workflow include the embedding of translation technology at the front end of the workflow (e.g., translation memory technology, specialized lexicons, and machine translation), technology-generated “seed translation”, a new human work role called “paralinguist” to assess the “seed translation” and assign an appropriate translator/post-editor, and new human translation strategies including federated search of online dictionaries and collaborative translation.