Dependency Parsing as Sequence Labeling with Head-Based Encoding and Multi-Task Learning

Abstract

Dependency parsing as sequence labeling has recently proved to be a relevant alternative to the traditional transitionand graph-based approaches. It offers a good trade-off between parsing accuracy and speed. However, recent work on dependency parsing as sequence labeling ignore the pre-processing time of Part-of-Speech tagging – which is required for this task – in the evaluation of speed while other studies showed that Part-of-Speech tags are not essential to achieve state-ofthe-art parsing scores. In this paper, we compare the accuracy and speed of shared and stacked multi-task learning strategies – as well as a strategy that combines both – to learn Part-of-Speech tagging and dependency parsing in a single sequence labeling pipeline. In addition, we propose an alternative encoding of the dependencies as labels which does not use Part-of-Speech tags and improves dependency parsing accuracy for most of the languages we evaluate.