Using the rules presented in Table 1 we create visual encodings for them (see Table 2, source side), and finally translate them into natural language using a Phrase-based SMT engine (Koehn et al., 2003).
given a source visual expression f, our aim is to find an equivalent target natural language descriptionê that maximizes the posterior probability: Most recent SMT work models the posterior P(e|f) directly  using a log-linear combination of several models where: and the decision decision rule is given by: where h k (f, e) is a scoring function representing important features for the translation of f into e. Examples include the language model of the target language, a reordering model, or several translation models.