Castor/sm_modified_cnn

SM model

References:

1. 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

Requirements

nltk==3.2.2
numpy==1.11.3
pytorch==0.1.12
gensim==1.0.1 

The code uses torchtext for text processing. Set torchtext:

git clone https://github.com/pytorch/text.git
cd text

#use this commit number
git reset --hard 2980f1bc39ba6af332c5c2783da8bee109796d4c
python setup.py install

We use trec_eval for evaluation:

cd eval
tar -xvf trec_eval.9.0.tar.gz
cd trec_eval.9.0 
make
cd ../..

Setup

Clone and create the dataset:

git clone https://github.com/castorini/data.git
git clone https://github.com/castorini/Castor.git

You should you see the following tree:

.
├── Castor
│   ├── README.md
│   ├── baseline_results.tsv
│   ├── idf_baseline
│   ├── kim_cnn
│   ├── mp_cnn
│   ├── setup.py
│   ├── sm_cnn
│   └── sm_modified_cnn
└── data
    ├── GloVe
    ├── ParagramEmbeddings
    ├── README.md
    ├── SimpleQuestions_v2
    ├── TrecQA
    ├── WikiQA
    ├── msrvid
    ├── requirements.txt
    ├── sick
    ├── twitterPPDB
    ├── utils
    └── word2vec

To create the dataset:

cd Castor/sm_modified_cnn/
./create_dataset.sh

Training

Download the word2vec model from [here] (https://drive.google.com/file/d/0B2u_nClt6NbzUmhOZU55eEo4QWM/view?usp=sharing) and copy it to the data/ folder.

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

To train on GPU 0 with static configuration:

python train.py --mode static --gpu 0

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 

Evaluation

The performance on TrecQA dataset:

TrecQA:

Best dev

Metric	rand	static	non-static	multichannel
MAP	0.8096	0.8162	0.8387	0.8274
MRR	0.8560	0.8918	0.9058	0.8818

Test

Metric	rand	static	non-static	multichannel
MAP	0.7441	0.7524	0.7688	0.7641
MRR	0.8172	0.8012	0.8144	0.8174

WikiQA:

Best dev

Metric	rand	static	non-static	multichannel
MAP	0.7109	0.7204	0.7049	0.7245
MRR	0.7169	0.7234	0.7075	0.7259

Test

Metric	rand	static	non-static	multichannel
MAP	0.6313	0.6378	0.6455	0.6476
MRR	0.6522	0.6542	0.6689	0.6646

NB: The results on WikiQA are based on the SM model hyperparameters.

To create your own word2vec.pt file

• Download word2vec from here to the data/ folder

python utils.py --input data/aquaint+wiki.txt.gz.ndim=50.bin