Improving language generation from feature-rich tree-structured data with relational graph convolutional encoders

Abstract

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the system design and the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the second rank in the deep track without using data augmentation techniques or additional components (such as a re-ranker).