Automatic Standardization of Colloquial Persian

• URL: http://arxiv.org/abs/2012.05879v1
• Date: Thu, 10 Dec 2020 18:39:26 GMT
• Title: Automatic Standardization of Colloquial Persian
• Authors: Mohammad Sadegh Rasooli, Farzane Bakhtyari, Fatemeh Shafiei, Mahsa Ravanbakhsh, Chris Callison-Burch
• Abstract summary: Most natural language processing tools for Persian assume that the text is in standard form. This paper describes a simple and effective standardization approach based on sequence-to-sequence translation.
• Score: 15.192770717442302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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