# LLM-enhanced Self-training for Cross-domain Constituency Parsing

arXiv: https://arxiv.org/abs/2311.02660 · HTML: https://mlsanity.com/paper/2311.02660/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Self-rewarding alignment and self-training, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Placed in

- Synthetic data and self-improvement for LLMs / [Self-rewarding alignment and self-training](https://mlsanity.com/synth/self-rewarding-alignment-and-self-training/)

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [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)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Revisiting Self-Training for Neural Sequence Generation](https://mlsanity.com/paper/1909.13788/) (2019)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [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)
- [Automated Annotation with Generative AI Requires Validation](https://mlsanity.com/paper/2306.00176/) (2023)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [Deep Contextualized Self-training for Low Resource Dependency Parsing](https://mlsanity.com/paper/1911.04286/) (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)
- [Adaptive Self-training for Few-shot Neural Sequence Labeling](https://mlsanity.com/paper/2010.03680/) (2020)
- [Cross-Domain Generalization of Neural Constituency Parsers](https://mlsanity.com/paper/1907.04347/) (2019)
- [Targeted Data Generation: Finding and Fixing Model Weaknesses](https://mlsanity.com/paper/2305.17804/) (2023)
- [Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training](https://mlsanity.com/paper/2009.11538/) (2020)

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

- [Self-Correction Makes LLMs Better Parsers](https://mlsanity.com/paper/2504.14165/) (2025)
- [Better Benchmarking LLMs for Zero-Shot Dependency Parsing](https://mlsanity.com/paper/2502.20866/) (2025)
- [Cross-domain Chinese Sentence Pattern Parsing](https://mlsanity.com/paper/2402.16311/) (2024)
- [Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing](https://mlsanity.com/paper/2505.20976/) (2025)

## Abstract (arXiv)

Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-training methods rely on limited and potentially low-quality raw corpora. To overcome this limitation, we propose enhancing self-training with the large language model (LLM) to generate domain-specific raw corpora iteratively. For the constituency parsing, we introduce grammar rules that guide the LLM in generating raw corpora and establish criteria for selecting pseudo instances. Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance. Moreover, the combination of grammar rules and confidence criteria for pseudo-data selection yields the highest performance in the cross-domain constituency parsing.