A Low Cost Machine Translation Method for Cross-Lingual Information Retrieval

In one form or another language translation is a necessary part of cross-lingual information retrieval systems. Often times this is accomplished using machine translation systems. However, machine translation systems offer low quality for their high costs. This paper proposes a machine translation method that is low cost while improving translation quality. This is done by utilizing multiple webbased translation services to negate the high cost of translation. A best translation is chosen from the candidates using either consensus translation selection or statistical analysis. Which to use is determined by a heuristic rule that takes into account that most web based translation services are of similar quality and that machine translation still produces relatively poor results. By choosing the best translation the method is able to increase translation quality over the base systems, which is verified by the experimentation.