Improving Question Answering on SQuAD 2.0: Exploring the QANet Architecture

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In this project, we investigated QANet - an end-to-end, non-recurrent model that is based on the use of convolutions and self-attention. Our first goal was to reimplement the QANet model from scratch and compare its performance to that of our baseline BiDAF - a model that relies on recurrent neural networks with attention. Both of the QA answering systems were tested on SQuAD 2.0 which includes both questions that are answerable given a context and questions that are not answerable given the context. Finally, after evaluation of our "vanilla" QANet and investigation of related work, we implemented an extended model called EQuANT. The model adds an additional output to explicitly predict the answerability of a question given the context. Our best model (QANet with tuned hyper-parameters) achieves F1 = 57.56 and EM = 54.66 on the developmental set, and F1 = 56.76 and EM = 53.34 on the test set.