ParsBERT: Transformer-based Model for Persian Language Understanding

• URL: http://arxiv.org/abs/2005.12515v2
• Date: Sun, 31 May 2020 13:09:07 GMT
• Title: ParsBERT: Transformer-based Model for Persian Language Understanding
• Authors: Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri
• Abstract summary: This paper proposes a monolingual BERT for the Persian language (ParsBERT) It shows its state-of-the-art performance compared to other architectures and multilingual models. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones.
• Score: 0.7646713951724012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance. However, these models are usually focused on English, leaving other languages to multilingual models with limited resources. This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models. Also, since the amount of data available for NLP tasks in Persian is very restricted, a massive dataset for different NLP tasks as well as pre-training the model is composed. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones and improves the state-of-the-art performance by outperforming both multilingual BERT and other prior works in Sentiment Analysis, Text Classification and Named Entity Recognition tasks.

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