Using a cross-language information retrieval approach to improve Computer-Aided Translation performance

Translation memories play a vital role in Computer-Aided Translation (CAT). Approaches to align sentences from parallel corpora are generally used to construct translation memories. In this paper, we present, on the one hand, a hybrid approach to align sentences combining different information sources (bilingual lexicon, sentence length and sentence position, semantic similarity), and on the other hand, a Computer-Aided Translation prototype based on a cross-language information retrieval approach. This approach consists in building a database of source and target sentences augmented with grammatical information of words and considering the sentence to translate as a "query" to that database. The Computer-Aided Translation prototype has been experimented on a set of examples and the experimental results we obtained are encouraging and outperform the current CAT tools.