  Tamil, a Dravidian language of South Asia, is a highly diglossic language
with two very different registers in everyday use: Literary Tamil (preferred in
writing and formal communication) and Spoken Tamil (confined to speech and
informal media). Spoken Tamil is under-supported in modern NLP systems. In this
paper, we release IruMozhi, a human-annotated dataset of parallel text in
Literary and Spoken Tamil. We train classifiers on the task of identifying
which variety a text belongs to. We use these models to gauge the availability
of pretraining data in Spoken Tamil, to audit the composition of existing
labelled datasets for Tamil, and to encourage future work on the variety.
