Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
Table 1: Comparison of loss functions in terms of case-insensitive BLEU scores on the validation set for Chinese-to-English translation.
['Loss', 'BLEU']
This can be possibly attributed to the fact that a larger sum does not necessarily lead to increased agreement. For example, while 0.9 + 0.1 hardly agree, 0.2 + 0.2 perfectly does. Therefore, ΔSOA seems to be an inaccurate measure of agreement.