Question Answering on SQuAD
نویسنده
چکیده
In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.
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