Victor Yang
5fd31a0434
* update nce-sm * refactor code, update torchtext * use shared evaluation * refactor code, use shared data loader * refactor code * refactor code * refactor code according to Michael's great suggestions * update readme and requirement * update datasets and readme * update data loader * add space between + * update path name, update readme * update data loader and dataset name * refactor code * update readme
3.7 KiB
NCE-SM-CNN model PyTorch Implementation
References:
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Aliaksei _S_everyn and Alessandro _M_oschitti. 2015. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15). ACM, New York, NY, USA, 373-382. DOI: http://dx.doi.org/10.1145/2766462.2767738
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Jinfeng Rao, Hua He, and Jimmy Lin. Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016), pages 1913-1916.
The code uses torchtext for text processing. Set torchtext:
git clone https://github.com/pytorch/text.git
cd text
python setup.py install
Download the word2vec model from [here] (https://drive.google.com/file/d/0B2u_nClt6NbzUmhOZU55eEo4QWM/view?usp=sharing)
and copy it to the Castor/data/word2vec folder.
Training the model
You can train the SM model for the 4 following configurations:
- random - the word embedddings are initialized randomly and are tuned during training
- static - the word embeddings are static (Severyn and Moschitti, SIGIR'15)
- non-static - the word embeddings are tuned during training
- multichannel - contains static and non-static channels for question and answer conv layers
python train.py --no_cuda --mode rand --batch_size 64 --neg_num 8 --dev_every 50 --patience 100 --dataset trec
NB: pass --no_cuda to use CPU
The trained model will be save to:
saves/static_best_model.pt
Testing the model
python main.py --trained_model saves/trec/multichannel_best_model.pt --batch_size 64 --no_cuda --dataset trec
Evaluation
The performance on TrecQA dataset:
Without NCE
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.7441 | 0.7524 | 0.7688 | 0.7641 |
| MRR | 0.8172 | 0.8012 | 0.8144 | 0.8174 |
Pairwise + Random Sample with neg_num = 8
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.7427 | 0.7546 | 0.7614 | 0.7645 |
| MRR | 0.8151 | 0.8061 | 0.8162 | 0.8270 |
Pairwise + Max Neg Sample with neg_num = 8
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.7437 | 0.7602 | 0.7752 | 0.7664 |
| MRR | 0.8151 | 0.8109 | 0.8270 | 0.8347 |
The performance on WikiQA dataset:
Without NCE
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.6472 | 0.6500 | 0.6620 | 0.6542 |
| MRR | 0.664 | 0.6693 | 0.6806 | 0.6722 |
Pairwise + Random Sample with neg_num = 8
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.6655 | 0.6816 | 0.6697 | 0.6739 |
| MRR | 0.6831 | 0.6992 | 0.6929 | 0.6925 |
Pairwise + Max Neg Sample with neg_num = 8
| Metric | rand | static | non-static | multichannel |
|---|---|---|---|---|
| MAP | 0.6687 | 0.6796 | 0.6854 | 0.6851 |
| MRR | 0.6864 | 0.6977 | 0.7012 | 0.7035 |
Optional Dependencies
To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to TensorBoard. These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add --tensorboard when running train.py and open TensorBoard in the browser.
pip install tensorboardX
pip install tensorflow-tensorboard
Usage:
tensorboard --host 0.0.0.0 --port 5001 --logdir runs