NCE-SM model

References:

• 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

• 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.gitcd 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:

1. random - the word embedddings are initialized randomly and are tuned during training

2. static - the word embeddings are static (Severyn and Moschitti, SIGIR'15)

3. non-static - the word embeddings are tuned during training

4. 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 1000

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/trecqa/multichannel_best_model.pt --batch_size 64 --no_cuda

Evaluation

The performance on TrecQA dataset:

Without NCE

Max Neg Sample

To be added

Pairwise + Max Neg Sample with neg_num = 8

The performance on WikiQA dataset:

To be added