  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.
