The Unanswerable Gap: An Exploration of Approaches for Question Answering on SQuAD 2.0

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In this project, we implemented models that were trained and evaluated using the Stanford Question Answering Dataset (SQuAD). For a majority of our models, we incorporated character-level embeddings in order to strengthen the system's understanding of the semantics and syntax of each context and question. Our implementations fall into two main categories: modifying the baseline Bidirectional Attention Flow (BiDAF) model and implementing the Dynamic Coattention Network from scratch. We found that the baseline BiDAF model with character-level embeddings performed the best and received an EM/F1 score of 61.771/65.089 on the test set.