A Chunk-Based Reordering Model for Phrase-Based SMT Systems

This paper proposed a novel reordering model based on the reordering of source language chunks. This model is used as a preprocessing step of phrase-based translation models and could be well integrated with them. At the same time, as a chunk-based model, syntax information could be concerned in the process of reordering while the entire parsing of the source sentence is not required. Two experiments were carried out and the results showed that the proposed model could improve the performance of a phrase-based statistical machine translation (SMT) system greatly

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