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.

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.

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The present study has surveyed post-editor trainees' views and attitudes before and after the introduction of speech technology as a front end to a computer-aided translation workbench. The aim of the survey was (i) to identify attitudes and perceptions among post-editor trainees before performing a post-editing task using automatic speech recognition (ASR); and (ii) to assess the degree to which post-editors' attitudes and expectations to the use of speech technology changed after actually using it. The survey was based on two questionnaires: the first one administered before the participants performed with the ASR system and the second one at the end of the session, once they have actually used ASR while post-editing machine translation outputs. Overall, the results suggest that the surveyed posteditor trainees tended to report a positive view of ASR in the context of post-editing and they would consider adopting ASR as an input method for future post-editing tasks.