In this paper we describe the METIS-II system and its evaluation on each of the language pairs: Dutch, German, Greek, and Spanish to English. The METIS-II system envisaged developing a data-driven approach in which no parallel corpus is required, and in which no full parser or extensive rule sets are needed. We describe evalution on a development test set and on a test set coming from Europarl, and compare our results with SYSTRAN. We also provide some further analysis, researching the impact of the number and source of the reference translations and analysing the results according to test text type. The results are expectably lower for the METIS system, but not at an unatainable distance from a mature system like SYSTRAN.

In the present article, a hybrid approach is proposed for implementing a machine translation system using a large monolingual corpus coupled with a bilingual lexicon and basic NLP tools. In the first phase of the METIS system, a source language (SL) sentence, after being tagged, lemmatised and translated by a flat lemma-to-lemma lexicon, was matched against a tagged and lemmatised target language (TL) corpus using a pattern matching algorithm. In the second phase, translations are generated by combining sub-sentential structures. In this paper, the main features of the second phase are discussed while the system architecture and the corresponding translation approach are presented. The proposed methodology is illustrated with examples of the translation process.

This paper presents the activities of Euromat (European Machine Translation) office in Greece, which has been functioning as a centre for Machine Translation Services for the Greek Public Sector since 1994. It describes the user profile, his/her attitude towards MT, strategies of promotion and the collected corpus for the first three years. User data were collected by questionnaires, interviews and corpus statistics. The general conclusions which have come out from our surveys are discussed.