Why do models often attend to salient words,and how does this evolve throughout training?

This work tries to understand the black box of attention training. Early on in training, the LSTM attention models first learn how to translate individual words from the bag of words co-occurrence statistics, which then drives the learning of the attention. The authors propose a framework explaining why attention weights obtained by standard training often correlate with saliency, and how multi-head attention can increase performance by improving the training dynamics rather than expressiveness.

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