The phrase table is considered to be the main bilingual resource for the phrase-based statistical
machine translation (PBSMT) model. During translation, a source sentence is decomposed
into several phrases. The best match of each source phrase is selected among several target-side
counterparts within the phrase table, and processed by the decoder to generate a sentence-level
translation. The best match is chosen according to several factors, including a set of bilingual
features. PBSMT engines by default provide four probability scores in phrase tables which are
considered as the main set of bilingual features. Our goal is to enrich that set of features, as a
better feature set should yield better translations. We propose new scores generated by a Convolutional
Neural Network (CNN) which indicate the semantic relatedness of phrase pairs. We
evaluate our model in different experimental settings with different language pairs. We observe
significant improvements when the proposed features are incorporated into the PBSMT pipeline