  Indic languages like Hindi and Tamil are underrepresented in the natural
language processing (NLP) field compared to languages like English. Due to this
underrepresentation, performance on NLP tasks (such as search algorithms) in
Indic languages are inferior to their English counterparts. This difference
disproportionately affects those who come from lower socioeconomic statuses
because they consume the most Internet content in local languages. The goal of
this project is to build an NLP model that performs better than pre-existing
models for the task of extractive question-answering (QA) on a public dataset
in Hindi and Tamil. Extractive QA is an NLP task where answers to questions are
extracted from a corresponding body of text. To build the best solution, we
used three different models. The first model is an unmodified cross-lingual
version of the NLP model RoBERTa, known as XLM-RoBERTa, that is pretrained on
100 languages. The second model is based on the pretrained RoBERTa model with
an extra classification head for the question answering, but we used a custom
Indic tokenizer, then optimized hyperparameters and fine tuned on the Indic
dataset. The third model is based on XLM-RoBERTa, but with extra finetuning and
training on the Indic dataset. We hypothesize the third model will perform best
because of the variety of languages the XLM-RoBERTa model has been pretrained
on and the additional finetuning on the Indic dataset. This hypothesis was
proven wrong because the paired RoBERTa models performed the best as the
training data used was most specific to the task performed as opposed to the
XLM-RoBERTa models which had much data that was not in either Hindi or Tamil.
