Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Table 1: Results of the models on the Chinese-English translation
['Model', 'MT-03', 'MT-04', 'MT-05', 'MT-06', 'Ave.']
As to the recent models on the same task with the same training data, we extract their results from their original articles. Compared with the baseline models, our model with the SACT for the softness of attention achieves better performance, with the advantages of BLEU score 2.94 over the conventional attention-based Seq2Seq model. The SACT effectively learns the temperature to control the softness of attention so that the model can utilize the information from the source-side contexts more efficiently.