# Neural Combinatory Constituency Parsing

arXiv: https://arxiv.org/abs/2106.06689 · HTML: https://mlsanity.com/paper/2106.06689/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [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)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [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)
- [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)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [Strongly Incremental Constituency Parsing with Graph Neural Networks](https://mlsanity.com/paper/2010.14568/) (2020)
- [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)
- [Shift-Reduce Constituent Parsing with Neural Lookahead Features](https://mlsanity.com/paper/1612.00567/) (2016)
- [Encoder-Decoder Shift-Reduce Syntactic Parsing](https://mlsanity.com/paper/1706.07905/) (2017)
- [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)

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

- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)

## Abstract (arXiv)

We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theoretical sub-quadratic complexity and empirical linear complexity. The binary model achieves an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. Both the models with XLNet provide near state-of-the-art accuracies for English. Syntactic branching tendency and headedness of a language are observed during the training and inference processes for Penn Treebank, Chinese Treebank, and Keyaki Treebank (Japanese).