Previous machine comprehension (MC) datasets are either too small to train
end-to-end deep learning models, or not difficult enough to evaluate the
ability of current MC techniques. The newly released SQuAD dataset alleviates
these limitations, and gives us a chance to develop more realistic MC models.
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM)
model, which is an end-to-end system that directly predicts the answer
beginning and ending points in a passage. Our model first adjusts each
word-embedding vector in the passage by multiplying a relevancy weight computed
against the question. Then, we encode the question and weighted passage by
using bi-directional LSTMs. For each point in the passage, our model matches
the context of this point against the encoded question from multiple
perspectives and produces a matching vector. Given those matched vectors, we
employ another bi-directional LSTM to aggregate all the information and predict
the beginning and ending points. Experimental result on the test set of SQuAD
shows that our model achieves a competitive result on the leaderboard.

在 SQuAD 数据集上，我们提出了一种多角度上下文匹配模型，它是一种端到端系统，可以直接预测文本段落中答案的起点和终点。我们的模型通过使用双向 LSTM 来编码问题和加权段落，利用多个角度将文本上下文与编码问题进行匹配，并生成一个匹配向量，最终利用另一个双向 LSTM 汇总所有信息并预测起点和终点。在 SQuAD 测试集上，实验结果显示我们的模型达到了领先位置的竞争力。