BanglaNLG: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla

• URL: http://arxiv.org/abs/2205.11081v2
• Date: Tue, 24 May 2022 01:33:34 GMT
• Title: BanglaNLG: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
• Authors: Abhik Bhattacharjee, Tahmid Hasan, Wasi Uddin Ahmad, Rifat Shahriyar
• Abstract summary: This work presents a benchmark for evaluating natural language generation models in Bangla. We aggregate three challenging conditional text generation tasks under the BanglaNLG benchmark. Using a clean corpus of 27.5 GB of Bangla data, we pretrain BanglaT5, a sequence-to-sequence Transformer model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming mT5 (base) by up to 5.4%.
• Score: 21.47743471497797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language in the web domain. We aggregate three challenging conditional text generation tasks under the BanglaNLG benchmark. Then, using a clean corpus of 27.5 GB of Bangla data, we pretrain BanglaT5, a sequence-to-sequence Transformer model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming mT5 (base) by up to 5.4%. We are making the BanglaT5 language model and a leaderboard publicly available in the hope of advancing future research and evaluation on Bangla NLG. The resources can be found at https://github.com/csebuetnlp/BanglaNLG.

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