cuixianliang / ABCNN

Implementation of ABCNN(Attention-Based Convolutional Neural Network) on Tensorflow

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

This is the implementation of ABCNN, which is proposed by Wenpeng Yin et al., on Tensorflow.
It includes all 4 models below:

• BCNN

  		MAP	MRR
  BCNN(1 layer)	Results	0.6660	0.6813
  	Baseline	0.6629	0.6813
  BCNN(2 layer)	Results	0.6762	0.6871
  	Baseline	0.6593	0.6738

• ABCNN-1

  		MAP	MRR
  ABCNN-1(1 layer)	Results	0.6652	0.6755
  	Baseline	0.6810	0.6979
  ABCNN-1(2 layer)	Results	0.6702	0.6838
  	Baseline	0.6855	0.7023

• ABCNN-2

  		MAP	MRR
  ABCNN-2(1 layer)	Results	0.6660	0.6813
  	Baseline	0.6885	0.7023
  ABCNN-2(2 layer)	Results	------	------
  	Baseline	0.6879	0.7068

• ABCNN-3

  		MAP	MRR
  ABCNN-3(1 layer)	Results	0.6612	0.6682
  	Baseline	0.6914	0.7127
  ABCNN-3(2 layer)	Results	0.6571	0.6722
  	Baseline	0.6921	0.7105

Note:

• Implementation is now only focusing on AS task with WikiQA corpus. (I originally tried to deal with PI task with MSRP(Microsoft Research Paraphrase) corpus but it seems that model doesn't work without external features classifier requires.)
• My code has verified that BCNN works fine as the authors proposed. (watched even better results than the paper's.)
• In the case of ABCNNs, results are inferior to ones in the paper but somewhat competitive. Careful hyperparameter configuration and detailed re-examination may help to achieve optimized results.
• I doubt that there are some bugs on ABCNNs(especially ABCNN-2 which has 2 conv layers) and will keep watching codes. Please be careful when using the results.

Specification

• preprocess.py: preprocess (training, test) data and import word2vec to use.
• train.py: train a model with configs.
• test.py: test the trained model.
• ABCNN.py: Implementation of ABCNN models.
• show.py: pyplot codes for test results.
• utils.py: common util functions.
• MSRP_Corpus: MSRP corpus for PI.
• WikiQA_Corpus: WikiQA corpus for AS.
• models: saved models available on Tensorflow.
• experiments: test results on AS tasks.

Development Environment

• OS: Windows 10 (64 bit)
• Language: Python 3.5.3
• CPU: Intel Xeon CPU E3-1231 v3 3.4 GHz
• RAM: 16GB
• GPU support: GTX 970
• Libraries:
  • tensorflow 1.2.1
  • numpy 1.12.1
  • gensim 1.0.1
  • NLTK 3.2.2
  • scikit-learn 0.18.1
  • matplotlib 2.0.0

Requirements

This model is based on pre-trained Word2vec(GoogleNews-vectors-negative300.bin) by T.Mikolov et al.
You should download this file and place it in the root folder.

Execution

(training): python train.py --lr=0.08 --ws=4 --l2_reg=0.0004 --epoch=20 --batch_size=64 --model_type=BCNN --num_layers=2 --data_type=WikiQA

Paramters
--lr: learning rate
--ws: window_size
--l2_reg: l2_reg modifier
--epoch: epoch
--batch_size: batch size
--model_type: model type
--num_layers: number of convolution layers
--data_type: MSRP or WikiQA data

(test): python test.py --ws=4 --l2_reg=0.0004 --epoch=20 --max_len=40 --model_type=BCNN --num_layers=2 --data_type=WikiQA --classifier=LR

Paramters
--ws: window_size
--l2_reg: l2_reg modifier
--epoch: epoch
--max_len: max sentence length
--model_type: model type
--num_layers: number of convolution layers
--data_type: MSRP or WikiQA data
--classifier: Final layout classifier(model, LR, SVM)

MISC.

• Original code by the author.