# Stack-Pointer Networks for Dependency Parsing

arXiv: https://arxiv.org/abs/1805.01087 · HTML: https://mlsanity.com/paper/1805.01087/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 30 of 36)

- [Pushing the Limits of ChatGPT on NLP Tasks](https://mlsanity.com/paper/2306.09719/) (2023)
- [AMR Parsing with Action-Pointer Transformer](https://mlsanity.com/paper/2104.14674/) (2021)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Enhancing Grammatical Error Correction Systems with Explanations](https://mlsanity.com/paper/2305.15676/) (2023)
- [RST Parsing from Scratch](https://mlsanity.com/paper/2105.10861/) (2021)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [Headed-Span-Based Projective Dependency Parsing](https://mlsanity.com/paper/2108.04750/) (2021)
- [Measuring and Reducing Model Update Regression in Structured Prediction for NLP](https://mlsanity.com/paper/2202.02976/) (2022)
- [A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages](https://mlsanity.com/paper/2102.06551/) (2021)
- [A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://mlsanity.com/paper/2106.04216/) (2021)
- [A Conditional Splitting Framework for Efficient Constituency Parsing](https://mlsanity.com/paper/2106.15760/) (2021)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering](https://mlsanity.com/paper/2104.06239/) (2021)
- [Dependency Parsing with Bottom-up Hierarchical Pointer Networks](https://mlsanity.com/paper/2105.09611/) (2021)
- [ChatGPT is a Potential Zero-Shot Dependency Parser](https://mlsanity.com/paper/2310.16654/) (2023)
- [On the Capabilities of Pointer Networks for Deep Deductive Reasoning](https://mlsanity.com/paper/2106.09225/) (2021)
- [Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing](https://mlsanity.com/paper/2201.11374/) (2022)
- [Transition-based Semantic Role Labeling with Pointer Networks](https://mlsanity.com/paper/2205.10023/) (2022)
- [Syntactic Language Change in English and German: Metrics, Parsers, and Convergences](https://mlsanity.com/paper/2402.11549/) (2024)
- [An In-depth Study on Internal Structure of Chinese Words](https://mlsanity.com/paper/2106.00334/) (2021)
- [Algorithms for Weighted Pushdown Automata](https://mlsanity.com/paper/2210.06884/) (2022)
- [Structured Sentiment Analysis as Transition-based Dependency Graph Parsing](https://mlsanity.com/paper/2305.05311/) (2023)
- [DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit](https://mlsanity.com/paper/2310.09501/) (2023)
- [Pointer-Augmented Autoregressive Generation of Patent Claims with Joint Topology and Content Decoding](https://mlsanity.com/paper/2607.24040/) (2026)
- [Automatic Correction of Syntactic Dependency Annotation Differences](https://mlsanity.com/paper/2201.05891/) (2022)
- [Schema-Free Dependency Parsing via Sequence Generation](https://mlsanity.com/paper/2201.12407/) (2022)
- [Cross-lingual Inflection as a Data Augmentation Method for Parsing](https://mlsanity.com/paper/2205.09350/) (2022)
- [The Fragility of Multi-Treebank Parsing Evaluation](https://mlsanity.com/paper/2209.06699/) (2022)
- [Transferring Neural Potentials For High Order Dependency Parsing](https://mlsanity.com/paper/2306.10469/) (2023)
- [A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing](https://mlsanity.com/paper/2306.12018/) (2023)

## Abstract (arXiv)

We introduce a novel architecture for dependency parsing: stack-pointer networks (textbftextscStackPtr). Combining pointer networks~citepvinyals2015pointer with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The textscStackPtr parser benefits from the information of the whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet, the number of steps for building any (including non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with O(n2) time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performance on 21 of them.