Multi-Perspective Context Matching for Machine Comprehension

Understand

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.