# Efficient Constituency Parsing by Pointing

arXiv: https://arxiv.org/abs/2006.13557 · HTML: https://mlsanity.com/paper/2006.13557/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [A Conditional Splitting Framework for Efficient Constituency Parsing](https://mlsanity.com/paper/2106.15760/) (2021)
- [Investigating Non-local Features for Neural Constituency Parsing](https://mlsanity.com/paper/2109.12814/) (2021)
- [Neural Combinatory Constituency Parsing](https://mlsanity.com/paper/2106.06689/) (2021)
- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)
- [Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence](https://mlsanity.com/paper/2110.15931/) (2021)
- [High-order Joint Constituency and Dependency Parsing](https://mlsanity.com/paper/2309.11888/) (2023)

## Abstract (arXiv)

We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.