dksifoua / Question-Answering

State of the art of Neural Question Answering using PyTorch.

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bidaf docker drqa jenkins kubeflow kubernetes natural-language-processing pytorch qanet question-answering rnet

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Question Answering - QA

Question-Answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does.

In this repo, I implemented end-to-end neural networks model which aims to answer questions from a given passage.

HUMAN PERFORMANCE

EM: 86.831
F1: 89.452

TODO

[x] DrQA
[x] BiDAF
[ ] QANet
[ ] RNet

References

[0] Chen, D., Bolton, J., & Manning, C. D. (2016). A thorough examination of the cnn/daily mail reading comprehension task. arXiv preprint arXiv:1606.02858.
[1] Chen, D., Fisch, A., Weston, J., & Bordes, A. (2017). Reading wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051.