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Benjamin Bolte
2016-04-18 22:14:53 -04:00
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@@ -6,7 +6,7 @@ The original paper, which provided inspiriation for this project, can be found [
Most of this stuff is just a project for a class of mine, but in the process of writing it I wrote a few files that might be of interest to other people.
- `attention_lstm.py`: Attentional LSTM, based on the referenced paper, and others.
- `attention_lstm.py`: Attentional LSTM, based on the referenced paper, and others. One application used it for [image captioning](http://arxiv.org/pdf/1502.03044.pdf).
- `keras_attention_model.py`: Implementation of an attentional LSTM architecture for question-answer matching, which closely mimics one of the architectures described in the referenced paper. In particular, the model uses the attentional LSTM implementation in `attention_lstm.py` to generate a sentence embedding for each answer given a particular question, and fits the answer embedding to have a high cosine similarity with the question embedding.
- `word_embeddings.py`: A Word2Vec layer that uses the embeddings generated by Gensim's word2vec model to provide vectors in place of the Keras `Embedding` layer, which could help improve convergence, since fewer parameters need to be learned. Note that this requires generating a separate file with the word2vec weights, so it doesn't fit in very nicely with the Keras architecture.