Dependency Graph Parsing as Sequence Labeling

TL;DR

This paper extends sequence labeling methods for dependency parsing to support complex graph structures like semantic dependencies, achieving high efficiency and competitive accuracy.

Contribution

It introduces new linearizations that enable sequence labeling to handle unbounded, bounded, and cyclic dependency graphs, broadening the applicability of this approach.

Findings

High efficiency in dependency graph parsing

Accuracy close to state-of-the-art methods

Supports complex graph structures like semantic dependencies

Abstract

Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representations, such as semantic dependencies or enhanced universal dependencies, as they cannot handle reentrancy or cycles. By extending them, we define a range of unbounded and bounded linearizations that can be used to cast graph parsing as a tagging task, enlarging the toolbox of problems that can be solved under this paradigm. Experimental results on semantic dependency and enhanced UD parsing show that with a good choice of encoding, sequence-labeling dependency graph parsers combine high efficiency with accuracies close to the state of the art, in spite of their simplicity.