Machine Translation As a Tool in Second Language Learning
Don D. Anderson · CALICO Journal · 1995
The current major Machine Translation (MT) evaluation effort, funded by the Advanced Research Projects Agency (ARPA), shows that when compared to expert human translators, MT systems perform only about 65% as well on the average. In this paper it is argued that despite their overall poor performance, MT software can be used as a powerful focal point to improve second language (L2) skills. The paper describes the evaluation of Computronics Corporation's Targumatik (Hebrew—>English), a PC-based MT system running under DOS, and shows how each problem and potential obstruction to learning can be overcome by means of discovery procedures using a set of tools and procedures called the ‘learning algorithm.’