Papers › Multi-Perspective Context Matching for Machine Comprehension
Multi-Perspective Context Matching for Machine Comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza, Radu Florian
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | SQuAD1.1 | MPCM | EM | 65.5 | #3 of 3 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Multi-Perspective Matching (ensemble) | EM | 73.765 | #133 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Multi-Perspective Matching (ensemble) | F1 | 81.257 | #133 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Multi-Perspective Matching (single model) | EM | 70.387 | #162 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | Multi-Perspective Matching (single model) | F1 | 78.784 | #162 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | MPCM | EM | 66.1 | #44 of 55 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 dev | MPCM | F1 | 75.8 | #44 of 55 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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