# Deep Biaffine Attention for Neural Dependency Parsing

arXiv: https://arxiv.org/abs/1611.01734 · HTML: https://mlsanity.com/paper/1611.01734/

## Key facts

- Cited by 334 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2016.

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## Abstract (arXiv)

This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8% and 2.2%---and comparable to the highest performing transition-based parser (Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.