  Significant advances have been made in recent years on Natural Language
Processing with machines surpassing human performance in many tasks, including
but not limited to Question Answering. The majority of deep learning methods
for Question Answering targets domains with large datasets and highly matured
literature. The area of Nuclear and Atomic energy has largely remained
unexplored in exploiting non-annotated data for driving industry viable
applications. Due to lack of dataset, a new dataset was created from the 7000
research papers on nuclear domain. This paper contributes to research in
understanding nuclear domain knowledge which is then evaluated on Nuclear
Question Answering Dataset (NQuAD) created by nuclear domain experts as part of
this research. NQuAD contains 612 questions developed on 181 paragraphs
randomly selected from the IGCAR research paper corpus. In this paper, the
Nuclear Bidirectional Encoder Representational Transformers (NukeBERT) is
proposed, which incorporates a novel technique for building BERT vocabulary to
make it suitable for tasks with less training data. The experiments evaluated
on NQuAD revealed that NukeBERT was able to outperform BERT significantly, thus
validating the adopted methodology. Training NukeBERT is computationally
expensive and hence we will be open-sourcing the NukeBERT pretrained weights
and NQuAD for fostering further research work in the nuclear domain.
