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.

Corpus-based MT systems that analyse and generalise texts beyond the surface forms of words require generation tools to re-generate the various internal representations into valid target language (TL) sentences. While the generation of word-forms from lemmas is probably the last step in every text generation process at its very bottom end, token-generation cannot be accomplished without structural and morpho-syntactic knowledge of the sentence to be generated. As in many other MT models, this knowledge is composed of a target language model and a bag of information transferred from the source language. In this paper we establish an abstracted, linguistically informed, target language model. We use a tagger, a lemmatiser and a parser to infer a template grammar from the TL corpus. Given a linguistically informed TL model, the aim is to see what need be provided from the transfer module for generation. During computation of the template grammar, we simultaneously build up for each TL sentence the content of the bag such that the sentence can be deterministically reproduced. In this way we control the completeness of the approach and will have an idea of what pieces of information we need to code in the TL bag.