Mapping a word into two different vectors can help the model disambiguate the role that it plays in different context.
biaffine Scoring In the standard NMT context, given a target recurrent output vector h  considering that in a traditional classification task, the distribution of classes is often uneven, and that the output layer of the model normally includes a bias term designed to capture the prior probability P (y i = c) of each class, with the rest of the model focusing on learning the likelihood of each class given the data P (y i = c|x i ), (Dozat and Manning, 2017) introduced the bias terms into the bilinear attention to address such uneven problem, resulting in a biaffine transformation.