Abstract
We investigate the problem of efficiently incorporating high-order features into neural
graph-based dependency parsing. Instead of
explicitly extracting high-order features from
intermediate parse trees, we develop a more
powerful dependency tree node representation
which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations
of GNN’s updating and aggregation functions.
Experiments on PTB show that our parser
achieves the best UAS and LAS on PTB
(96.0%, 94.3%) among systems without using
any external resources.