Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR 2017: https://arxiv.org/abs/1703.03130 .
For binary sentiment classification on imdb dataset run :
python classification.py "binary"
For multiclass classification on reuters dataset run :
python classification.py "multiclass"
You can change the model parameters in the model_params.json file
Other tranining parameters like number of attention hops etc can be configured in the config.json file.
If you want to use pretrained glove embeddings , set the use_embeddings parameter to "True" ,default is set to False. Do not forget to download the glove.6B.50d.txt and place it in the glove folder.
- Classification using self attention
- Regularization using Frobenius norm
- Gradient clipping
- Visualizing the attention weights
Instead of pruning ,used averaging over the sentence embeddings.
After training, the model is tested on 100 test points. Attention weights for the 100 test data are retrieved and used to visualize over the text using heatmaps. A file visualization.html gets saved in the visualization/ folder after successful training. The visualization code was provided by Zhouhan Lin (@hantek). Many thanks.
Below is a shot of the visualization on few datapoints.
Training accuracy 93.4% Tested on 1000 points with 90.2% accuracy