# Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

arXiv: https://arxiv.org/abs/1806.04168 · HTML: https://mlsanity.com/paper/1806.04168/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 30 of 32)

- [Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing](https://mlsanity.com/paper/2302.01524/) (2023)
- [What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code](https://mlsanity.com/paper/2202.06840/) (2022)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [AST-Probe: Recovering abstract syntax trees from hidden representations of pre-trained language models](https://mlsanity.com/paper/2206.11719/) (2022)
- [Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation](https://mlsanity.com/paper/2203.00281/) (2022)
- [Controllable Video Captioning with an Exemplar Sentence](https://mlsanity.com/paper/2112.01073/) (2021)
- [Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities](https://mlsanity.com/paper/2212.10017/) (2022)
- [A Conditional Splitting Framework for Efficient Constituency Parsing](https://mlsanity.com/paper/2106.15760/) (2021)
- [Linguistic Blind Spots of Large Language Models](https://mlsanity.com/paper/2503.19260/) (2025)
- [Have Attention Heads in BERT Learned Constituency Grammar?](https://mlsanity.com/paper/2102.07926/) (2021)
- [Cooperative Self-training of Machine Reading Comprehension](https://mlsanity.com/paper/2103.07449/) (2021)
- [The Limitations of Limited Context for Constituency Parsing](https://mlsanity.com/paper/2106.01580/) (2021)
- [Graph Parsing Networks](https://mlsanity.com/paper/2402.14393/) (2024)
- [Syntax-guided Localized Self-attention by Constituency Syntactic Distance](https://mlsanity.com/paper/2210.11759/) (2022)
- [Generating CCG Categories](https://mlsanity.com/paper/2103.08139/) (2021)
- [Emergent Linguistic Structures in Neural Networks are Fragile](https://mlsanity.com/paper/2210.17406/) (2022)
- [AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach](https://mlsanity.com/paper/2212.07043/) (2022)
- [StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure](https://mlsanity.com/paper/2305.05588/) (2023)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Part-of-Speech Tagger for Bodo Language using Deep Learning approach](https://mlsanity.com/paper/2401.03175/) (2024)
- [In-Order Chart-Based Constituent Parsing](https://mlsanity.com/paper/2102.04065/) (2021)
- [Neural Combinatory Constituency Parsing](https://mlsanity.com/paper/2106.06689/) (2021)
- [Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models](https://mlsanity.com/paper/2211.00479/) (2022)
- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)
- [Language Cognition and Language Computation -- Human and Machine Language Understanding](https://mlsanity.com/paper/2301.04788/) (2023)
- [Leveraging Grammar Induction for Language Understanding and Generation](https://mlsanity.com/paper/2410.04878/) (2024)
- [Unsupervised Full Constituency Parsing with Neighboring Distribution Divergence](https://mlsanity.com/paper/2110.15931/) (2021)
- [Unsupervised and Few-shot Parsing from Pretrained Language Models](https://mlsanity.com/paper/2206.04980/) (2022)
- [Order-sensitive Neural Constituency Parsing](https://mlsanity.com/paper/2211.00421/) (2022)
- [Joint Chinese Word Segmentation and Span-based Constituency Parsing](https://mlsanity.com/paper/2211.01638/) (2022)

## Abstract (arXiv)

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.