# Effective Inference for Generative Neural Parsing

arXiv: https://arxiv.org/abs/1707.08976 · HTML: https://mlsanity.com/paper/1707.08976/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 25 of 25)

- [BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions](https://mlsanity.com/paper/2406.15877/) (2024)
- [Grounding Language Models to Images for Multimodal Inputs and Outputs](https://mlsanity.com/paper/2301.13823/) (2023)
- [Structural Guidance for Transformer Language Models](https://mlsanity.com/paper/2108.00104/) (2021)
- [Probing for Incremental Parse States in Autoregressive Language Models](https://mlsanity.com/paper/2211.09748/) (2022)
- [Pushdown Layers: Encoding Recursive Structure in Transformer Language Models](https://mlsanity.com/paper/2310.19089/) (2023)
- [Effective Batching for Recurrent Neural Network Grammars](https://mlsanity.com/paper/2105.14822/) (2021)
- [Emergent Word Order Universals from Cognitively-Motivated Language Models](https://mlsanity.com/paper/2402.12363/) (2024)
- [Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale](https://mlsanity.com/paper/2403.08293/) (2024)
- [Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models](https://mlsanity.com/paper/2407.17406/) (2024)
- [A Conditional Splitting Framework for Efficient Constituency Parsing](https://mlsanity.com/paper/2106.15760/) (2021)
- [Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars](https://mlsanity.com/paper/2109.04939/) (2021)
- [Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models](https://mlsanity.com/paper/2109.11058/) (2021)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [Tree-Planted Transformers: Unidirectional Transformer Language Models with Implicit Syntactic Supervision](https://mlsanity.com/paper/2402.12691/) (2024)
- [If Attention Serves as a Cognitive Model of Human Memory Retrieval, What is the Plausible Memory Representation?](https://mlsanity.com/paper/2502.11469/) (2025)
- [In-Order Chart-Based Constituent Parsing](https://mlsanity.com/paper/2102.04065/) (2021)
- [Composition, Attention, or Both?](https://mlsanity.com/paper/2210.12958/) (2022)
- [Is Structure Dependence Shaped for Efficient Communication?: A Case Study on Coordination](https://mlsanity.com/paper/2410.10556/) (2024)
- [A Systematic Study of Compositional Syntactic Transformer Language Models](https://mlsanity.com/paper/2506.22978/) (2025)
- [GiLT: Augmenting Transformer Language Models with Dependency Graphs](https://mlsanity.com/paper/2605.15562/) (2026)
- [Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion](https://mlsanity.com/paper/2204.08644/) (2022)
- [Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures](https://mlsanity.com/paper/2305.18915/) (2023)
- [Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis](https://mlsanity.com/paper/2605.15440/) (2026)
- [Does Traversal Order Matter? A Systematic Study of Tree Traversal Methods in Transformer Grammars](https://mlsanity.com/paper/2606.16836/) (2026)
- [A Scaled-Up Empirical Study of Syntactic Language Models](https://mlsanity.com/paper/2610.16837/) (2026)

## Abstract (arXiv)

Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable. We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models. We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space. Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.