Cost-sensitive active learning for computer-assisted translation

• Published in: Pattern recognition letters Vol. 37; pp. 124 - 134
• Main Authors: González-Rubio, Jesús, Casacuberta, Francisco
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2014
• Subjects: Active learning; Computer-assisted translation; Interactive machine translation; Online learning; Interactive machine translation; Computer-assisted translation; Active learning; Online learning
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2013.06.007

•We present a new active learning framework for computed assisted translation.•Our goal is to make the translation process as efficient as possible for human translators.•We implement efficient techniques to detect informative sentences.•We use online learning techniques to update the translation model with user feedback.•Results show that our method allows to double the productivity of conventional approaches. Machine translation technology is not perfect. To be successfully embedded in real-world applications, it must compensate for its imperfections by interacting intelligently with the user within a computer-assisted translation framework. The interactive–predictive paradigm, where both a statistical translation model and a human expert collaborate to generate the translation, has been shown to be an effective computer-assisted translation approach. However, the exhaustive supervision of all translations and the use of non-incremental translation models penalizes the productivity of conventional interactive–predictive systems. We propose a cost-sensitive active learning framework for computer-assisted translation whose goal is to make the translation process as painless as possible. In contrast to conventional active learning scenarios, the proposed active learning framework is designed to minimize not only how many translations the user must supervise but also how difficult each translation is to supervise. To do that, we address the two potential drawbacks of the interactive–predictive translation paradigm. On the one hand, user effort is focused to those translations whose user supervision is considered more “informative”, thus, maximizing the utility of each user interaction. On the other hand, we use a dynamic machine translation model that is continually updated with user feedback after deployment. We empirically validated each of the technical components in simulation and quantify the user effort saved. We conclude that both selective translation supervision and translation model updating lead to important user-effort reductions, and consequently to improved translation productivity.