Recent Research in the Field of Example-Based Machine Translation

Abstract

Machine Translation (MT) is a multi-disciplinary, fast evolving research domain, which makes use of almos all computa ional methods known in artificial intell- igence. Paradigms as differen as logic programming, case-based reasoning, ge- netic algorithms, artificial neural networks, probabilistic and statistic methods have all been employed for the translation task. However, none of the proposed methods has ye led to overall satisfactory results and given birth to a universal machine ranslation engine. Instead, the search for what translation quality and what coverage of the system one would realistically need, what methods and knowledge resources are required for tha end and how much one is willing to invest, is a research domain in itself. Until the end of the eighties, MT was strongly dominated by rule-based systems which deduce translations of natural language texts based on a bilingual lexicon and a grammar formalism. Source language sentences were analyzed with a monolingual grammar and the source language representation was mapped into the target language by means of a transfer grammar. The target representations were then refined and adapted to the target language requirements. Since the beginning of the nineties, huge corpora of bilingual translated texts have been made available in computer-readable format. This empirical knowledge has given raise to a new paradigm in machine translation, that of corpus-based machine translation. Corpus-based machine translation (CBMT) systems make use of reference translations which are assumed to be ideal with respect to text type and domain, target reader and its intention.