This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries.

We describe an open source workbench that offers advanced computer aided translation (CAT) functionality: post-editing machine translation (MT), interactive translation prediction (ITP), visualization of word alignment, extensive logging with replay mode, integration with eye trackers and e-pen.

Based on an architecture that allows to combine statistical machine translation (SMT) with rule-based machine translation (RBMT) in a multi-engine setup, we present new results that show that this type of system combination can actually increase the lexical coverage of the resulting hybrid system, at least as far as this can be measured via BLEU score.

CASMACAT is a modular, web-based translation workbench that offers advanced functionalities for computer-aided translation and the scientific study of human translation: automatic interaction with machine translation (MT) engines and translation memories (TM) to obtain raw translations or close TM matches for conventional post-editing; interactive translation prediction based on an MT engine's search graph, detailed recording and replay of edit actions and translator's gaze (the latter via eye-tracking), and the support of e-pen as an alternative input device.The system is open source sofware and interfaces with multiple MT systems.

Received: date / Accepted: date Abstract We conducted a field trial in computer-assisted professional translation to compare Interactive Translation Prediction (ITP) against conventional postediting (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translation hypothesis that is then edited by a professional translator (hence "post-editing"), ITP constantly updates the translation hypothesis in real time in response to user edits. Our study involved nine professional translators and four reviewers working with the webbased CasMaCat workbench. Various new interactive features aiming to assist the post-editor were also tested in this trial. Our results show that even with little training, ITP can be as productive as conventional PE in terms of the total time required to produce the final translation. Moreover, in the ITP setting translators require fewer key strokes to arrive at the final version of their translation.