A Fully Attention-Based Information Retriever
This addresses the need for faster and more efficient natural language processing models, though it is incremental as it builds on existing attention mechanisms.
The paper tackled the problem of improving efficiency in question-answering by proposing a fully attention-based architecture, achieving competitive results on the SQuAD dataset with fewer parameters and faster learning and inference than rival methods.
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.