  The Iranian Persian language has two varieties: standard and colloquial. Most
natural language processing tools for Persian assume that the text is in
standard form: this assumption is wrong in many real applications especially
web content. This paper describes a simple and effective standardization
approach based on sequence-to-sequence translation. We design an algorithm for
generating artificial parallel colloquial-to-standard data for learning a
sequence-to-sequence model. Moreover, we annotate a publicly available
evaluation data consisting of 1912 sentences from a diverse set of domains. Our
intrinsic evaluation shows a higher BLEU score of 62.8 versus 61.7 compared to
an off-the-shelf rule-based standardization model in which the original text
has a BLEU score of 46.4. We also show that our model improves
English-to-Persian machine translation in scenarios for which the training data
is from colloquial Persian with 1.4 absolute BLEU score difference in the
development data, and 0.8 in the test data.
