Improving Interactive Machine Translation via Mouse Actions

Abstract

Although Machine Translation (MT) is a very active research field which is receiving an increasing amount of attention from the research community, the results that current MT systems are capable of producing are still quite far away from perfection. Because of this, and in order to build systems that yield correct translations, human knowledge must be integrated into the translation process, which will be carried out in our case in an Interactive-Predictive (IP) framework. In this paper, we show that considering Mouse Actions as a significant information source for the underlying system improves the productivity of the human translator involved. In addition, we also show that the initial translations that the MT system provides can be quickly improved by an expert by only performing additional Mouse Actions. In this work, we will be using word graphs as an efficient interface between a phrase-based MT system and the IP engine. © 2008 Association for Computational Linguistics.