Proactive Learning for Building Machine Translation Systems for Minority Languages

Building machine translation (MT) for many minority languages in the world is a serious challenge. For many minor languages there is little machine readable text, few knowledgeable linguists, and little money available for MT development. For these reasons, it becomes very important for an MT system to make best use of its resources, both labeled and unlabeled, in building a quality system. In this paper we argue that traditional active learning setup may not be the right fit for seeking annotations required for building a Syntax Based MT system for minority languages. We posit that a relatively new variant of active learning, Proactive Learning, is more suitable for this task.