# Investigating Non-local Features for Neural Constituency Parsing

arXiv: https://arxiv.org/abs/2109.12814 · HTML: https://mlsanity.com/paper/2109.12814/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 27 Sep 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)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (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)
- [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)
- [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020)
- [Head-Driven Phrase Structure Grammar Parsing on Penn Treebank](https://mlsanity.com/paper/1907.02684/) (2019)
- [Neural Extractive Text Summarization with Syntactic Compression](https://mlsanity.com/paper/1902.00863/) (2019)
- [Hierarchically-Refined Label Attention Network for Sequence Labeling](https://mlsanity.com/paper/1908.08676/) (2019)
- [In-Order Transition-based Constituent Parsing](https://mlsanity.com/paper/1707.05000/) (2017)
- [What's Going On in Neural Constituency Parsers? An Analysis](https://mlsanity.com/paper/1804.07853/) (2018)
- [Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles](https://mlsanity.com/paper/1612.06475/) (2016)
- [A Tree-based Decoder for Neural Machine Translation](https://mlsanity.com/paper/1808.09374/) (2018)
- [Cross-Domain Generalization of Neural Constituency Parsers](https://mlsanity.com/paper/1907.04347/) (2019)
- [Improving Constituency Parsing with Span Attention](https://mlsanity.com/paper/2010.07543/) (2020)
- [Uncertainty-Aware Label Refinement for Sequence Labeling](https://mlsanity.com/paper/2012.10608/) (2020)
- [Neural CRF Parsing](https://mlsanity.com/paper/1507.03641/) (2015)
- [Efficient Constituency Parsing by Pointing](https://mlsanity.com/paper/2006.13557/) (2020)
- [Two Local Models for Neural Constituent Parsing](https://mlsanity.com/paper/1808.04850/) (2018)
- [Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels](https://mlsanity.com/paper/2011.00470/) (2020)

## Built on by (most cited first, 4 of 4)

- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)
- [Constituency Parsing using LLMs](https://mlsanity.com/paper/2310.19462/) (2023)
- [Opinion Tree Parsing for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2306.08925/) (2023)

## Abstract (arXiv)

Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the self-attentive parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1). Our parser also achieves better or competitive performance in multilingual and zero-shot cross-domain settings compared with the baseline.