The phrase–based statistical machine translation (PBSMT) model can be viewed as a
log-linear combination of translation and language model features. Such a model typically relies
on the phrase table as the main resource for bilingual knowledge, which in its most basic form
consists of aligned phrases, along with four probability scores. These scores only indicate the cooccurrence
of phrase pairs in the training corpus, and not necessarily their semantic relatedness.
The basic phrase table is also unable to incorporate contextual information about the segments
where a particular phrase tends to occur. In this paper, we define six new features which express
the semantic relatedness of bilingual phrases. Our method utilizes both source and target side
information to enrich the phrase table. The new features are inferred from a bilingual corpus by
a neural network (NN). We evaluate our model on the English–Farsi (En–Fa) and English–Czech
(En–Cz) pairs and observe considerable improvements in the all En↔Fa and En↔Cz directions