Nobody is Perfect: ATR's Hybrid Approach to Spoken Language Translation
Michael D. Paul, Takao Doi, Young-Sook Hwang, Kenji Imamura, Hideo Okuma, Eiichiro Sumita · 2005
This paper describes ATR’s hybrid approach to spoken language translation and it’s application to the IWSLT 2005 translation task. Multiple corpus-based translation engines are used to translate the same input, whereby the best translation among the element MT outputs is selected according to statistical models. The evaluation results of the Japanese-to-English and Chinese-to-English translation tasks for different training data conditions showed the potential of the proposed hybrid approach and revealed new directions in how to improve the current system performance. 1.