Python versioin
Python 3.6.2
Package Requirements
Torch 1.0.1.post2, Numpy 1.15.1, Tqdm 4.31.1, Rouge 0.3.2, Scipy 1.1.0
python build_data.py
python train.py -batch 256 -epoch 20 -n_layers 2 -save_model
Some model parameters can be set in the utils/config.py
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self.cell: Setting rnn unit is 'LSTM' or 'GRU'.
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self.bidirectional: Whether the setting unit is bidirectional or not.
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self.attn_flag: Different Calculating Functions of Attention Mechanism
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loung: Loung attention
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bahdanau: Bahdanau attention
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self.enc_attn: Whether or not to apply self-attention mexhanism at encoder
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self.intra_decoder: Whether or not to apply self-attention mexhanism at decoder
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self.cnn:
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cnn=0: No feature extraction of sentences
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cnn=1: Using CNN to extract sentence features and connect the feature vectors to the final hidden state of the encoder.
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cnn=2: Using CNN to extract sentence features and connect the feature vectors to the encoder outputs.
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python beam_test.py -b 5
PS Setting up the file of the model.
| Model | R-1 | R-2 | R-L |
|---|---|---|---|
| Bi-GRU | 0.2917 | 0.1244 | 0.2347 |
| Bi-GRU + Bahdanau | 0.3035 | 0.1411 | 0.2547 |
| Bi-LSTM + Bahdanau | 0.3623 | 0.2198 | 0.3231 |
| Bi-LSTM + Luong | 0.3621 | 0.2321 | 0.3301 |
| Bi-LSTM + Luong + enc_attn | 0.3920 | 0.2615 | 0.3452 |
| Bi-LSTM + Luong + inter_dec | 0.3847 | 0.2471 | 0.3402 |
| Bi-LSTM + Luong + CNN(1) | 0.3881 | 0.2602 | 0.3586 |
| Bi-LSTM + Luong + CNN(2) | 0.3914 | 0.2645 | 0.3701 |