Comments (1)
The paper does not seem to mention different RNN's trained for different sentences. We force the classifier in the end to learn a shared representation among sequence of sentences and sequence of words. This shared representation helps the model to generalise well to unseen data.
There is nothing stopping you from training different GRU's though, but I think it will be a futile effort.
from attention-networks-for-classification.
Related Issues (18)
- Performance comparison to baseline models HOT 1
- how could I run this on Python 3 HOT 3
- Sentence model bug when GRU are not bidirectional HOT 1
- Dimensionalities of word minibatch and Embedding layer don't match HOT 1
- Having 2 optimizers HOT 3
- Init hidden state for the 2nd sentence onward HOT 2
- RNN mask issue
- the google drive can not open
- imdb_final.json HOT 2
- 你好 可以把数据和文本预处理的代码发我一下么 我这边访问不了
- Could I ask about the dataset `imdb_final.json`
- An example for save this model HOT 2
- single data prediction HOT 1
- Can not run the script HOT 1
- Loss can start as NaN HOT 5
- the pad_batch function is error? HOT 1
- transpose? HOT 3
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from attention-networks-for-classification.