A Structured Self-attentive Sentence Embedding

Overview

Structured Self-attentive sentence embeddings

Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR 2017: https://arxiv.org/abs/1703.03130 .

USAGE:

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.

Implemented:

  • Classification using self attention
  • Regularization using Frobenius norm
  • Gradient clipping
  • Visualizing the attention weights

Instead of pruning ,used averaging over the sentence embeddings.

Visualization:

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. alt text

Training accuracy 93.4% Tested on 1000 points with 90.2% accuracy


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Comments
  • Penalization term

    Penalization term

    In the original paper, the penalization term is equal to the Frobenius norm squared. In your implementation you did not consider the power 2. Did you try comparing both cases? frobenius

    opened by moussaKam 6
  • Experiments

    Experiments

    Hello dear @kaushalshetty ,

    I'm trying to run your experiments, but I can get the same results. I made the same changes than webdizz, but the accuracy are always 50%. This default configuration should generate the 93.4% accuracy result?

    Thanks

    opened by heukirne 2
  • about classfication.py

    about classfication.py

    when i run this sentence : classification_type = sys.argv[1] IndexError: list index out of range I don't know how to slove, can you tell me the solution?thanks

    opened by Catherine-HFUT 1
  • How

    How

    Hello,thanks for your codes!I am trying to learn Self-Attention. But cant find the input files, Can you please give the sample input and other required files to run this example ?

    opened by TingEn-Li 1
Owner
Kaushal Shetty
Compute Is All You Need!!!
Kaushal Shetty
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