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codes for ai challenger 2018 machine reading comprehension
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QANet
capsuleNet
data
README.md
start.sh
vote_ser_new_word.py

README.md

Intruduction

This project is my code for AiChallenger2018 Opinion Questions Machine Reading Comprehension. This project mainly has 2 models which are implemented in Tensorflow.

  • Model-1 is based on QANet, but I rewrite it in some details.
  • Model-2 is based on capsuleNet which mainly from freefuiiismyname's project

Dependencies

  • Python 3.6
  • Tensorflow 1.9.0
  • tqdm
  • gensim

Data Sample

{ “query_id”:1, “query”:“维生c可以长期吃吗”, “url”: “xxx”, “passage”: “每天吃的维生素的量没有超过推荐量的话是没有太大问题的。”, “alternatives”:”可以|不可以|无法确定”, “answer”:“可以” }

Performance

I train each model on one GTX1080ti for 30 epochs, and report the best Performance on dev set. We finally run Model-1 on testa, the accuracy is 73.2.

Model Accuracy
Model-1(ensembled) 73.66
Model-2(ensembled) 73.85
1&2 ensembled 76.62

Project Structure

  • capsuleNet: Model-2's codes
  • data: Model-2's data
  • QANet: Model-1's codes and data
  • start.sh: example for usage
  • vote_ser_new_word.py: vote ensemble file

Details

you can see more details in /QANet/README.md and /capsuleNet/README.md

Reference

some codes are borrowed from :

NLPLearn/QANet

freefuiiismyname/capsule-mrc

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