A review of neural approaches to the Question Answering task
Will Needham, City, University of London, Northampton Square, London, EC1V 0HB
The Question Answering task, whereby a system receives a plain language question from a user and returns a concise answer from a corpus of documents, has received considerable attention from academia and the commercial world since mid-way through the 20th century. This paper offers a concise overview of this literature, focussing on recent advancements of the state-of-the-art achieved by neural network-based
approaches. The rate of change of these advancements is considerable and has left a sparse landscape of analysis and research still to be conducted. My main contribution in this paper is to shine a light on these gaps in the literature, offering inspiration for future research in this domain.
Information Retrieval · Question Answering · Neural Networks · Word embeddings · Pre-trained language models