This paper describes an evaluation experiment designed to determine groups of subjects who prefer reading MT outputs to reading the original text. Our approach can be applied to any language pairs, but we will explain the methodology by taking English to Japanese translation as an example. In the case of E-J MT, it can be assumed that main users are Japanese and that most of them have some knowledge of English. It is often the case, in the case of E-J MT systems, that those people who are comfortable with reading English do not find E-J MT outputs useful, and in many cases, they would rather prefer reading the original English text. On the other hand, E- J MT outputs prove to be useful to those who find it hard to read the original English texts. We have used the reading comprehension part of the Test Of English for International Communication (TOEIC) to determine the threshold English ability level, dividing these two user groups.

MT users have to build “user dictionaries” in order to obtain high-quality translation results. However, building dictionaries needs time and labor. In order to meet the speed of the information flow in the global network society, we need to have common formats for sharing dictionaries among different MT systems, and a new way of dictionary authorization, that is “social filtering”.

In this article we outline a basic approach to treating metonymy properly in a multilingual machine translation system. This is the first attempt at treating metonymy in an machine translation environment. The approach is guided by the differences of acceptability of metonymy which were obtained by our comparative survey among three languages, English, Chinese, and Japanese. The characteristics of the approach are as follows:(1)Influences of the context, individuals, and familiality with metonymy are not used.(2) An actual acceptability of each metonymic expression is not realized directly.(3) Grouping metonymic examples into patterns is determined by the acceptability judgement of the speakers surveyed as well as the analysts' intuition.(4) The analysis and generation components treat metonymy differently using the patterns.(5) The analysis component accepts a wider range of metonymy than the actual results of the survey, and the generation component treats metonymy more strictly than the actual results.We think that the approach is a starting point for more sophisticated approaches to translation in a multilingual machine translation environment.