  We explore the impact of leveraging the relatedness of languages that belong
to the same family in NLP models using multilingual fine-tuning. We hypothesize
and validate that multilingual fine-tuning of pre-trained language models can
yield better performance on downstream NLP applications, compared to models
fine-tuned on individual languages. A first of its kind detailed study is
presented to track performance change as languages are added to a base language
in a graded and greedy (in the sense of best boost of performance) manner;
which reveals that careful selection of subset of related languages can
significantly improve performance than utilizing all related languages. The
Indo-Aryan (IA) language family is chosen for the study, the exact languages
being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script
barrier is crossed by simple rule-based transliteration of the text of all
languages to Devanagari. Experiments are performed on mBERT, IndicBERT, MuRIL
and two RoBERTa-based LMs, the last two being pre-trained by us. Low resource
languages, such as Oriya and Punjabi, are found to be the largest beneficiaries
of multilingual fine-tuning. Textual Entailment, Entity Classification, Section
Title Prediction, tasks of IndicGLUE and POS tagging form our test bed.
Compared to monolingual fine tuning we get relative performance improvement of
up to 150% in the downstream tasks. The surprise take-away is that for any
language there is a particular combination of other languages which yields the
best performance, and any additional language is in fact detrimental.
