  Machine Reading Comprehension (MRC) is an important topic in the domain of
automated question answering and in natural language processing more generally.
Since the release of the SQuAD 1.1 and SQuAD 2 datasets, progress in the field
has been particularly significant, with current state-of-the-art models now
exhibiting near-human performance at both answering well-posed questions and
detecting questions which are unanswerable given a corresponding context. In
this work, we present Enhanced Question Answer Network (EQuANt), an MRC model
which extends the successful QANet architecture of Yu et al. to cope with
unanswerable questions. By training and evaluating EQuANt on SQuAD 2, we show
that it is indeed possible to extend QANet to the unanswerable domain. We
achieve results which are close to 2 times better than our chosen baseline
obtained by evaluating a lightweight version of the original QANet architecture
on SQuAD 2. In addition, we report that the performance of EQuANt on SQuAD 1.1
after being trained on SQuAD2 exceeds that of our lightweight QANet
architecture trained and evaluated on SQuAD 1.1, demonstrating the utility of
multi-task learning in the MRC context.
