  Recurrent neural networks are now the state-of-the-art in natural language
processing because they can build rich contextual representations and process
texts of arbitrary length. However, recent developments on attention mechanisms
have equipped feedforward networks with similar capabilities, hence enabling
faster computations due to the increase in the number of operations that can be
parallelized. We explore this new type of architecture in the domain of
question-answering and propose a novel approach that we call Fully Attention
Based Information Retriever (FABIR). We show that FABIR achieves competitive
results in the Stanford Question Answering Dataset (SQuAD) while having fewer
parameters and being faster at both learning and inference than rival methods.
