Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | ENCODE,Computer science,Artificial intelligence,Deep learning,Machine learning,Comprehension,Test set |
DocType | Volume | Citations |
Journal | abs/1612.04211 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiguo Wang | 1 | 354 | 24.64 |
| Haitao Mi | 2 | 479 | 23.40 |
| wael hamza | 3 | 198 | 15.84 |
| Radu Florian | 4 | 924 | 91.44 |