Weighted posets: Learning surface order from dependency trees
William Dyer · 2019
This paper presents a novel algorithm for generating a surface word order for a sentence given its dependency tree using a two-stage process.Using dependency-based word embeddings and a Graph Neural Network, the algorithm first learns how to rewrite a dependency tree as a partially ordered set (poset) with edge-weights representing dependency distance.The subsequent topological sort of this poset reflects a surface word order.The algorithm is evaluated against a naive baseline of average dependency distances across 14 languages, performing well in terms of rank correlation and resulting rate of projectivity based on Universal Dependencies corpora.