Bilingually Motivated Word Segmentation for SMT

We introduce a bilingually motivated word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Our approach is motivated from the insight that PB-SMT systems can be improved by optimising the input representation to reduce the predictive power of translation models. We firstly present an approach to optimise the existing segmentation of both source and target languages for PBSMT and demonstrate the effectiveness of this approach using a Chinese– English MT task, i.e. to measure the influence of the segmentation on the performance of PB-SMT systems. We report a 5.44% relative increase in Bleu score and a consistent increase according to other metrics. We then generalise this method for Chinese word segmentation without relying on any segmenters and show that using our segmentation PB-SMT can achieve more consistent state-of-the-art performance across two domains. There are two main advantages of our approach. First of all, it is adapted to the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains.