TITLE: IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages 

ABSTRACT: Natural Language Generation (NLG) for non-English languages is hampered by
the scarcity of datasets in these languages. In this paper, we present the
IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic
languages. We focus on five diverse tasks, namely, biography generation using
Wikipedia infoboxes, news headline generation, sentence summarization,
paraphrase generation and, question generation. We describe the created
datasets and use them to benchmark the performance of several monolingual and
multilingual baselines that leverage pre-trained sequence-to-sequence models.
Our results exhibit the strong performance of multilingual language-specific
pre-trained models, and the utility of models trained on our dataset for other
related NLG tasks. Our dataset creation methods can be easily applied to
modest-resource languages as they involve simple steps such as scraping news
articles and Wikipedia infoboxes, light cleaning, and pivoting through machine
translation data. To the best of our knowledge, the IndicNLG Benchmark is the
first NLG benchmark for Indic languages and the most diverse multilingual NLG
dataset, with approximately 8M examples across 5 tasks and 11 languages. The
datasets and models are publicly available at
https://ai4bharat.iitm.ac.in/indicnlg-suite.
Natural Language Generation (NLG) for non-English languages is hampered by
the scarcity of datasets in these languages. In this paper, we present the
IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic
languages. We focus on five diverse tasks, namely, biography generation using
Wikipedia infoboxes, news headline generation, sentence summarization,
paraphrase generation and, question generation. We describe the created
datasets and use them to benchmark the performance of several monolingual and
multilingual baselines that leverage pre-trained sequence-to-sequence models.
Our results exhibit the strong performance of multilingual language-specific
pre-trained models, and the utility of models trained on our dataset for other
related NLG tasks. Our dataset creation methods can be easily applied to
modest-resource languages as they involve simple steps such as scraping news
articles and Wikipedia infoboxes, light cleaning, and pivoting through machine
translation data. To the best of our knowledge, the IndicNLG Benchmark is the
first NLG benchmark for Indic languages and the most diverse multilingual NLG
dataset, with approximately 8M examples across 5 tasks and 11 languages. The
datasets and models are publicly available at
https://ai4bharat.iitm.ac.in/indicnlg-suite.
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
http://arxiv.org/abs/2203.05437v2