# What's Going On in Neural Constituency Parsers? An Analysis

arXiv: https://arxiv.org/abs/1804.07853 · HTML: https://mlsanity.com/paper/1804.07853/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 22 of 22)

- [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)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [A Character-level Span-based Model for Mandarin Prosodic Structure Prediction](https://mlsanity.com/paper/2203.16922/) (2022)
- [LLM-enhanced Self-training for Cross-domain Constituency Parsing](https://mlsanity.com/paper/2311.02660/) (2023)
- [Approximating CKY with Transformers](https://mlsanity.com/paper/2305.02386/) (2023)
- [Head-driven Phrase Structure Parsing in O(n3) Time Complexity](https://mlsanity.com/paper/2105.09835/) (2021)
- [Lexicon-injected Semantic Parsing for Task-Oriented Dialog](https://mlsanity.com/paper/2211.14508/) (2022)
- [Linguistic-style-aware Neural Networks for Fake News Detection](https://mlsanity.com/paper/2301.02792/) (2023)
- [Cross-Lingual Constituency Parsing for Middle High German: A Delexicalized Approach](https://mlsanity.com/paper/2308.04645/) (2023)
- [To be Continuous, or to be Discrete, Those are Bits of Questions](https://mlsanity.com/paper/2406.07812/) (2024)
- [An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese](https://mlsanity.com/paper/2411.17270/) (2024)
- [In-Order Chart-Based Constituent Parsing](https://mlsanity.com/paper/2102.04065/) (2021)
- [Neural Sequence Segmentation as Determining the Leftmost Segments](https://mlsanity.com/paper/2104.07217/) (2021)
- [An In-depth Study on Internal Structure of Chinese Words](https://mlsanity.com/paper/2106.00334/) (2021)
- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)
- [Prosodic segmentation for parsing spoken dialogue](https://mlsanity.com/paper/2105.12667/) (2021)
- [Nested Named Entity Recognition as Holistic Structure Parsing](https://mlsanity.com/paper/2204.08006/) (2022)
- [Order-sensitive Neural Constituency Parsing](https://mlsanity.com/paper/2211.00421/) (2022)
- [Prosodic features improve sentence segmentation and parsing](https://mlsanity.com/paper/2302.12165/) (2023)
- [Cross-domain Chinese Sentence Pattern Parsing](https://mlsanity.com/paper/2402.16311/) (2024)
- [Lexicalized Constituency Parsing for Middle Dutch: Low-resource Training and Cross-Domain Generalization](https://mlsanity.com/paper/2601.07008/) (2026)

## Abstract (arXiv)

A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity. The goal of this work is to analyze the extent to which information provided directly by the model structure in classical systems is still being captured by neural methods. To this end, we propose a high-performance neural model (92.08 F1 on PTB) that is representative of recent work and perform a series of investigative experiments. We find that our model implicitly learns to encode much of the same information that was explicitly provided by grammars and lexicons in the past, indicating that this scaffolding can largely be subsumed by powerful general-purpose neural machinery.