  Text simplification is the process of splitting and rephrasing a sentence to
a sequence of sentences making it easier to read and understand while
preserving the content and approximating the original meaning. Text
simplification has been exploited in NLP applications like machine translation,
summarization, semantic role labeling, and information extraction, opening a
broad avenue for its exploitation in comprehension-based question-answering
downstream tasks. In this work, we investigate the effect of text
simplification in the task of question-answering using a comprehension context.
We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset.
  Firstly, we outline each step in the dataset creation pipeline, including
style transfer, thresholding of sentences showing correct transfer, and offset
finding for each answer. Secondly, we verify the quality of the transferred
sentences through various methodologies involving both automated and human
evaluation. Thirdly, we benchmark the newly created corpus and perform an
ablation study for examining the effect of the simplification process in the
SQuAD-based question answering task. Our experiments show that simplification
leads to up to 2.04% and 1.74% increase in Exact Match and F1, respectively.
Finally, we conclude with an analysis of the transfer process, investigating
the types of edits made by the model, and the effect of sentence length on the
transfer model.
