Integrating Online and Active Learning in a Computer-Assisted Translation Workbench

Abstract

This chapter describes a pilot study aiming at testing the integration of online and active learning features into the computer-assisted translation workbench developed within the CASMACAT project. These features can be used to take advantage of the new knowledge implicitly provided by human experts when they generate new translations. Online learning (OL) allows the system to learn from user feedback in real time by incrementally adapting the parameters of the statistical models involved in the translation process. On the other hand, active learning (AL) determines those sentences that need to be supervised by the user so as to maximize the final translation quality minimizing user effort and, at the same time, improving the statistical model parameters. We investigate the effect of these features on translation productivity, using interactive translation prediction (ITP) as a baseline. ITP is a computer assisted translation approach where the user interactively collaborates with a statistical machine translation system to generate high quality translations. User activity data was collected from ten translators using key-logging and eye-tracking. We found that ITP with OL performs better than standard ITP, especially in terms of typing effort required from the user to generate correct translations. Additionally, ITP with AL provides better translation quality than standard ITP for the same levels of user effort.