# Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing

arXiv: https://arxiv.org/abs/2212.08458 · HTML: https://mlsanity.com/paper/2212.08458/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Multilingual Constituency Parsing with Self-Attention and Pre-Training](https://mlsanity.com/paper/1812.11760/) (2018)
- [Recurrent Neural Network Grammars](https://mlsanity.com/paper/1602.07776/) (2016)
- [Grammar as a Foreign Language](https://mlsanity.com/paper/1412.7449/) (2014)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Cloze-driven Pretraining of Self-attention Networks](https://mlsanity.com/paper/1903.07785/) (2019)
- [Straight to the Tree: Constituency Parsing with Neural Syntactic Distance](https://mlsanity.com/paper/1806.04168/) (2018)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [Strongly Incremental Constituency Parsing with Graph Neural Networks](https://mlsanity.com/paper/2010.14568/) (2020)
- [What's Going On in Neural Constituency Parsers? An Analysis](https://mlsanity.com/paper/1804.07853/) (2018)
- [Generalizing Natural Language Analysis through Span-relation Representations](https://mlsanity.com/paper/1911.03822/) (2019)
- [Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles](https://mlsanity.com/paper/1612.06475/) (2016)
- [Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://mlsanity.com/paper/1904.09745/) (2019)
- [Improving Constituency Parsing with Span Attention](https://mlsanity.com/paper/2010.07543/) (2020)
- [Shift-Reduce Constituent Parsing with Neural Lookahead Features](https://mlsanity.com/paper/1612.00567/) (2016)
- [Efficient Constituency Parsing by Pointing](https://mlsanity.com/paper/2006.13557/) (2020)
- [A Span-based Linearization for Constituent Trees](https://mlsanity.com/paper/2004.14704/) (2020)
- [Neural Combinatory Constituency Parsing](https://mlsanity.com/paper/2106.06689/) (2021)

## Built on by (most cited first, 1 of 1)

- [Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing](https://mlsanity.com/paper/2505.20976/) (2025)

## Abstract (arXiv)

Most recent studies on neural constituency parsing focus on encoder structures, while few developments are devoted to decoders. Previous research has demonstrated that probabilistic statistical methods based on syntactic rules are particularly effective in constituency parsing, whereas syntactic rules are not used during the training of neural models in prior work probably due to their enormous computation requirements. In this paper, we first implement a fast CKY decoding procedure harnessing GPU acceleration, based on which we further derive a syntactic rule-based (rule-constrained) CKY decoding. In the experiments, our method obtains 95.89 and 92.52 F1 on the datasets of PTB and CTB respectively, which shows significant improvements compared with previous approaches. Besides, our parser achieves strong and competitive cross-domain performance in zero-shot settings.