Comments (1)
I believe you are right. Uw and Us are the global context vectors that stores information about which words or sentences are most informative respectively. They are learned during the training process.
from hierarchical-attention-networks.
Related Issues (20)
- some error in yelp_prepare.py HOT 4
- ValueError in running worker.py HOT 12
- How to make `TensorBoard Projector` work.
- Why use orthogonal_initializer ?
- Error While Running yelp_prepare.py HOT 3
- Is the embedding initialized with a pre-trained one? HOT 2
- GRU VS LSTM HOT 1
- Mask for attention weight
- Getting same sentence level outputs for very different documents. Can someone please help.
- Embeddings for special tokens/padding?
- dev accuracy: nan???
- en-core-web-sm needs to be installed beforehand
- Same cell for word and sentence level HOT 3
- Performance on the paper's dataset HOT 6
- Won't the code leads to different input shape for different batch?
- Visualize word and sentence attention weight as color coded in the paper HOT 1
- Performance on Yelp 15
- Implementation using tf.contrib.seq2seq. HOT 7
- Attention layer output HOT 5
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from hierarchical-attention-networks.