This is a repo for Q&A Mathing, includes some deep learning models, such as CNN、RNN.
- CNN. Basic CNN model from 《Applying Deep Learning To Answer Selection: A Study And An Open Task》
- RNN. RNN seems the best model on Insurance-QA dataset.
- SWEM. SWEM is the fastest, and has good effect on other datasets, such as WikiQA ..., but is seems not so good on Insurance-QA dataset. I think that, SWEM is more suitable for Q&Q matching, not Q&A matching.
It's hard to say which model is the best in other datasets, you have to choose the most suitable model for you.
More models are on the way, pay attention to the updates.
- tensorflow 1.4.0
- python3.5
margin loss version
| Model/Score | Ins_qa_top1_precision | quora_best_prec |
|---|---|---|
| CNN | 62% | None |
| LSTM+CNN | 68% | None |
| SWEM | <55% | None |
logloss version
| Model/Score | Insqa_top1_precision | quora_best_prec |
|---|---|---|
| CNN | None | 79.60% |
| LSTM+CNN | None | None |
| SWEM | <40% | 82.69% |
Change configuration to your own environment, just like data pathes
vim config.py
Data processing
python3 gen.py
Run CNN model
cd ./cnn/tensorflow && python3 insqa_train.py
It will take few hours(thousands of epoches) to train this model on a single GPU.
- You can get Insurance-QA data from here https://github.com/shuzi/insuranceQA
- You can get Quora data from here http://qim.ec.quoracdn.net/quora_duplicate_questions.tsv
- CNN and RNN textual classification repo https://github.com/white127/TextClassification_CNN_RNN
- 《Applying Deep Learning To Answer Selection: A Study And An Open Task》