An Approach Towards Question Answering Using Apposite Context
3 Pages Posted: 3 Sep 2019
Date Written: August 31, 2019
Question Answering (QA) is concerned with building systems that automatically answer questions given by humans in a natural language. Most of the successful approaches for these systems rely on Recurrent Neural Networks (RNNs). But, running them over long documents is prohibitively slow. Traditional models for QA optimize using cross entropy loss on the start and end positions of the answer. But, these models do not consider word overlapping between actual and predicted ones and thus, may results in wrong output. The proposed system puts forward a QA model that combines cross entropy loss with word overlapping. Another significance is that, the context being fed into the QA model is shortened by selecting only the relevant sentences with respect to the input question, from the source document. The model is implemented with the aid of a mechanism called Dynamic Coattention Network (DCN). Dataset used for the implementation of this system is Stanford Question Answering Dataset (SQuAD).
Keywords: Question Answering, Deep Neural Networks, Sentence selectors, Answer Generators, Natural Language Processing
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