In our model, we add a continuous vector representation of the language identity l (i.e., a language embedding) as an additional input to the neural networks in NDMV.
to predict an ATTACH rule probability P ATTACH (c|h, dir, val, l), we use a multilayer neural network that takes the embeddings of the head token h, valence val and language identity l as input, uses a weight matrix W dir specific to the direction dir in the first layer, and uses a weight matrix W c consisting of all the child POS tag vectors in the softmax output layer.