Vārta: A Large-Scale Headline-Generation Dataset for Indic Languages
Rahul Aralikatte Ziling Cheng Sumanth Doddapaneni Jackie Chi Kit Cheung
We present V\=arta, a large-scale multilingual dataset for headline
generation in Indic languages. This dataset includes 41.8 million news articles
in 14 different Indic languages (and English), which come from a variety of
high-quality sources. To the best of our knowledge, this is the largest
collection of curated articles for Indic languages currently available. We use
the data collected in a series of experiments to answer important questions
related to Indic NLP and multilinguality research in general. We show that the
dataset is challenging even for state-of-the-art abstractive models and that
they perform only slightly better than extractive baselines. Owing to its size,
we also show that the dataset can be used to pretrain strong language models
that outperform competitive baselines in both NLU and NLG benchmarks.