BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
Abhik Bhattacharjee, Tahmid Hasan, Wasi Uddin Ahmad, Rifat Shahriyar · 2023
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 sequenceto-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.