answer/- the neural network that combines all the solvers;essential_terms/- the model that assigns essential scores for each term in a question;multinli/- the neural network trained on the MultiNLI dataset;nlp_inference/- the neural network trained on the SNLI dataset;scitail/- the neural network trained on the SciTail dataset;rephrase/- the module that transforms questions into affirmative sentences;qa/- pre-trained neural networks on the SQuAD v1 dataset;protos/- protocol buffer definitions (for passing data around);wikipedia_indexer/- code for indexing Wikipedia dumps and science book collection. It also includes the IR solvers and candidate context extraction tool.
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Language Files Lines Blank Comment Code
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Java 63 19577 1807 2480 15290
Python 139 20634 2809 4141 13684
XML 1 197 5 0 192
Protobuf 4 118 25 2 91
Markdown 2 104 29 0 75
Bourne Shell 2 50 9 6 35
Makefile 2 47 17 0 30
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Total 213 40727 4701 6629 29397
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Collected using cgag/loc
| Dataset | Accuracy |
|---|---|
| ARC-Easy Test | 60.943% |
| ARC-Challenge Test | 26.706% |
Please contact gpirtoaca@gmail.com if you want to build and run the model. There are a lot of dependencies (both software and data sources) that need to be installed.
Improving Retrieval-Based Question Answering with Deep Inference Models