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BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
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This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain BanglaT5, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9% absolute gain and 32% relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.
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Cited by 2 Pith papers
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Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models
Fine-tuning mT5, BanglaT5, mBART50 and a basic Transformer on a newly translated Bengali math word problem dataset yields up to 97.3% solution accuracy on elementary arithmetic problems.
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BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion
Adding category, aspect, and sentiment labels to Bengali religious news articles improves transformer-based headline generation over a content-only baseline, with BanglaT5 reaching BLEU 18.61.
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