We propose new language models to represent phrasal structures by patterns extracted from parse trees. First, modified word trigram models are proposed. They are extracted from sentences analyzed by the preprocessing of the parser with knowledge. Since sentences are analyzed to create sub-trees of a few words, these trigram models can represent relations among a few neighbor words more strongly than conventional word trigram models. Second, word pattern models are used on these modified word trigram models. The word patterns are extracted from parse trees and can represent phrasal structures and much longer word-dependency than trigram models. Experimental results show that modified trigram models are more effective than traditional trigram models and that pattern models attain slight improvements over modified trigram models. Furthermore, additional experiments show that pattern models are more effective for long sentences.

In this paper we propose an objective method for assessing the capability of a speech translation system. It automates the translation paired comparison method, which gives a simple, easy to understand TOEIC score proposed by Sugaya et al., to succinctly evaluate a speech translation system. To avoid the expensive evaluation cost of the original method where large manual effort is required, the new objective method automates the procedure by employing an objective metric such as BLEU and DP-based measure. The evaluation results obtained by the proposed method are similar to those of the original method. Also, the proposed method is used to evaluate the usefulness of a speech translation system. It is then found that our speech translation system is useful in general, even to users with higher TOEIC score than the system's.