Question Answering with SQuAD: Variations on Multi-Perspective Context Matching
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چکیده
We implement multi-perspective context matching for the task of questionanswering on the SQuAD dataset and explore a variety of modifications to this core architecture. In our first modification, we compare the performance of GRUs with that of LSTMs in the original model. Next we attempt to predict the answer’s start index and length rather than its start and end indices. Finally, we introduce a variation of attention which we call “question summaries” into the model. This last modification proves most fruitful.
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تاریخ انتشار 2017