Given sentence s, the probability distribution of POS tags for word w is obtained using a one hidden-layer neural network applied to the corresponding encoder output h w 2 : For dependency parsing, words in a sentence are treated as nodes in a graph.
each word w in sentence s receives exactly one in-going edge (u, w, r) from head word u to its dependent w with relation r. We use a graph-based dependency parser similar to the one presented in , which treats dependency parsing as a classification task and its goal is to predict which in-going edge (u, w, r) connects to each word w. Mathematically, the probability of an edge is: where "score" is the scoring function: The bilinear classifier uses a weight matrix W r specific to the candidate relation and a weight matrix W shared across all dependency relations.