ViraPart: A Text Refinement Framework for ASR and NLP Tasks in Persian

• URL: http://arxiv.org/abs/2110.09086v2
• Date: Tue, 19 Oct 2021 11:24:30 GMT
• Title: ViraPart: A Text Refinement Framework for ASR and NLP Tasks in Persian
• Authors: Narges Farokhshad, Milad Molazadeh, Saman Jamalabbasi, Hamed Babaei Giglou, Saeed Bibak
• Abstract summary: We propose a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Persian language is an inflectional SOV language. This fact makes Persian a more uncertain language. However, using techniques such as ZWNJ recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text refinement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. First, used the BERT variant for Persian following by a classifier layer for classification procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90%, 92.13%, and 98.50%, respectively. Experimental results show that our proposed approach is very effective in text refinement for the Persian language.

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