To this end, we iteratively conduct sentiment predictions on all training instances.
at each iteration, we extract the context word with the maximum attention weight from each training instance to form attention supervision information, which can be used to guide the training of attention mechanism: in the case of correct prediction, we will remain this word to be considered; otherwise, the attention weight of this word is expected to be decreased.