  Bangla -- ranked as the 6th most widely spoken language across the world
(https://www.ethnologue.com/guides/ethnologue200), with 230 million native
speakers -- is still considered as a low-resource language in the natural
language processing (NLP) community. With three decades of research, Bangla NLP
(BNLP) is still lagging behind mainly due to the scarcity of resources and the
challenges that come with it. There is sparse work in different areas of BNLP;
however, a thorough survey reporting previous work and recent advances is yet
to be done. In this study, we first provide a review of Bangla NLP tasks,
resources, and tools available to the research community; we benchmark datasets
collected from various platforms for nine NLP tasks using current
state-of-the-art algorithms (i.e., transformer-based models). We provide
comparative results for the studied NLP tasks by comparing monolingual vs.
multilingual models of varying sizes. We report our results using both
individual and consolidated datasets and provide data splits for future
research. We reviewed a total of 108 papers and conducted 175 sets of
experiments. Our results show promising performance using transformer-based
models while highlighting the trade-off with computational costs. We hope that
such a comprehensive survey will motivate the community to build on and further
advance the research on Bangla NLP.
