  This work introduces Itihasa, a large-scale translation dataset containing
93,000 pairs of Sanskrit shlokas and their English translations. The shlokas
are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We
first describe the motivation behind the curation of such a dataset and follow
up with empirical analysis to bring out its nuances. We then benchmark the
performance of standard translation models on this corpus and show that even
state-of-the-art transformer architectures perform poorly, emphasizing the
complexity of the dataset.
