Viable Dependency Parsing as Sequence Labeling

TL;DR

This paper demonstrates that dependency parsing can be effectively reformulated as a sequence labeling task using BiLSTM models, achieving competitive accuracy and speed without complex algorithms.

Contribution

It introduces a simple, fast, and accurate sequence labeling approach for dependency parsing, challenging the notion that the technique is impractical.

Findings

Achieves competitive accuracy on PTB and UD treebanks.

Provides a fast and simple alternative to traditional parsing methods.

Maintains a good speed-accuracy tradeoff.

Abstract

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.