Advanced computer aided translation with a web-based workbench

We describe a web-based 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 epen. It is available open source and integrates with multiple MT systems. The goal of the CASMACAT project1 is to develop an advanced computer aided translation workbench. At the mid-point of the 3-year project, we release this tool as open source software. It already includes a wide range of novel advanced types of assistance and other functionalities that do not exist together in any other computer aided translation tool. The CASMACAT is working in close collaboration with the MATECAT project2, which also has the goal of developing a new open source webbased computer aided translation tool, and focuses mainly on post-editing machine translation, adaptation methods, and ease of use that make such a tool suitable for professional users. Through this combined effort, we hope to kickstart broader research into computer aided translation methods, facilitating diverse translation process studies, and reach volunteer and professional translators without advanced technical skills. The tool is developed as a web-based platform using HTML5 and Javascript in the Browser and PHP in the backend, supported by a CAT and MT server that run as independent process (both implemented in Python but integrating tools written in various other programming languages). http://www.casmacat. eu/ http://www.matecat. com/ 1 Related Work There is increasing evidence for productivity gains of professional translators when they post-edit machine translation output. For instance, Plitt and Masselot (2010) compare post-editing machine translation against unassisted translation in a web-based tool for a number of language pairs, showing productivity gains of up to 80%. Skadiņs et al. (2011) show a 30 percent increase for English-Latvian translation with a slight but acceptable degradation in quality. Federico et al. (2012) assess the benefit of offering machine translation output in addition to translation memory matches (marked as such) in a realistic work environment for translators working on legal and information technology documents. They observe productivity gains of 20-50%, roughly independent from the original translator speed and segment length, but with different results for different language pairs and domains. Moreover, Pouliquen et al. (2011) show that, aided by machine translation, non-professional post-editors may be able to create high-quality translations, comparable to a professional translation agency. So far, usage of machine translation technology has concentrated on human-computer interaction involving the human translator as a post-editor, but rarely involves the human translator influencing the decisions of the machine translation system. Recent efforts on building interactive machine translation systems include work by Langlais et al. (2000) and Barrachina et al. (2009). Both studies develop research systems looking into a tighter integration of human translators in MT processes by developing a prediction model that interactively suggests translations to the human translator as he or she types. Related work displays several word and phrase translation choices to human translators (Koehn, 2010). Figure 1: View for uploading new documents