AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation

• URL: http://arxiv.org/abs/2109.12068v1
• Date: Tue, 31 Aug 2021 02:02:10 GMT
• Title: AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation
• Authors: El Moatez Billah Nagoudi and AbdelRahim Elmadany and Muhammad Abdul-Mageed
• Abstract summary: We introduce a new benchmark for Arabic language generation (ARGEN) We pre-train three powerful Arabic-specific text-to-text Transformer based models and evaluate them on the two benchmarks. Our new models perform significantly better than mT5 and exceed MARBERT, the current state-of-the-art Arabic BERT-based model, on Arabic language understanding.
• Score: 6.021269454707625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format has recently been proposed as a simple, yet effective, transfer learning approach. Although a multilingual version of the T5 model (mT5) has been introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects--Arabic. For evaluation, we use an existing benchmark for Arabic language understanding and introduce a new benchmark for Arabic language generation (ARGEN). We also pre-train three powerful Arabic-specific text-to-text Transformer based models and evaluate them on the two benchmarks. Our new models perform significantly better than mT5 and exceed MARBERT, the current state-of-the-art Arabic BERT-based model, on Arabic language understanding. The models also set new SOTA on the generation benchmark. Our new models and are publicly released at https://github.com/UBC-NLP/araT5 and ARLGE will be released through the same repository.

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