Rediscovering R-NET: An Improvement and In-Depth Analysis on SQUAD 2.0

Question-answering is a discipline within the fields of information retrieval (IR) and natural language processing (NLP) that is concerned with building systems that automatically answer questions posed by humans. In this project, we address the question-answering task by attempting to improve the R-NET model. Specifically, our goals are to 1. reproduce R-NET and evaluate its performance on SQuAD 2.0 compared to that on the original SQuAD dataset and 2. change certain features of the R-NET model to further improve its accuracy on SQuAD 2.0. We present an implementation of R-NET using LSTM's instead of GRU's, larger embedding and hidden dimensions, higher dropout, and more layers that achieves an improvement in performance from our baseline R-NET model.