Viable Dependency Parsing as Sequence Labeling
This work provides a simpler and faster alternative to traditional dependency parsing algorithms, benefiting NLP practitioners by reducing computational complexity.
The authors tackled dependency parsing by reformulating it as a sequence labeling task, achieving competitive accuracy and speed compared to more complex methods, with results validated on PTB and UD treebanks.
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.