Training phrase-based SMT without explicit word aligment

International audienceThe machine translation systems usually build an initialword-to-word alignment, before training the phrase translation pairs.This approach requires a lot of matching between different single words ofboth considered languages. In this paper, we propose a new approach forphrase-based machine translation which does not require any word alignment.This method is based on inter-lingual triggers retrieved by MultivariateMutual Information. This algorithm segments sentences intophrases and fnds their alignments simultaneously. The main objectiveof this work is to build directly valid alignments between source andtarget phrases. The achieved results, in terms of performance are satisfactoryand the obtained translation table is smaller than the referenceone; this approach could be considered as an alternative to the classicalmethods

STATISTICAL MACHINE TRANSLATION IMPROVEMENT BASED ON PHRASE SELECTION

International audienceThis paper describes the importance of introducing a phrase-based language model in the process of machine translation. In fact, nowadays SMT are based on phrases for translation but their language models are based on classical ngrams. In this paper we introduce a phrase-based language model (PBLM) in the decoding process to try to match the phrases of a translation table with those predicted by a language model. Furthermore, we propose a new way to retrieve phrases and their corresponding translation by using the principle of conditional mutual information. The SMT developed will be compared to the baseline one in terms of BLEU, TER and METEOR. The experimental results show that the introduction of PBLM in the translation decoding improve the results