# Cross-domain Chinese Sentence Pattern Parsing

arXiv: https://arxiv.org/abs/2402.16311 · HTML: https://mlsanity.com/paper/2402.16311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [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)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [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)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [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)
- [What's Going On in Neural Constituency Parsers? An Analysis](https://mlsanity.com/paper/1804.07853/) (2018)
- [Cross-Domain Generalization of Neural Constituency Parsers](https://mlsanity.com/paper/1907.04347/) (2019)
- [UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization](https://mlsanity.com/paper/2211.09783/) (2022)
- [Targeted Data Generation: Finding and Fixing Model Weaknesses](https://mlsanity.com/paper/2305.17804/) (2023)
- [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://mlsanity.com/paper/2210.06277/) (2022)
- [Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM](https://mlsanity.com/paper/2304.11872/) (2023)
- [Shift-Reduce Constituent Parsing with Neural Lookahead Features](https://mlsanity.com/paper/1612.00567/) (2016)
- [Explicit Syntactic Guidance for Neural Text Generation](https://mlsanity.com/paper/2306.11485/) (2023)
- [A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing](https://mlsanity.com/paper/1904.04697/) (2019)
- [Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training](https://mlsanity.com/paper/2009.11538/) (2020)
- [LLM-enhanced Self-training for Cross-domain Constituency Parsing](https://mlsanity.com/paper/2311.02660/) (2023)

## Abstract (arXiv)

Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Partial syntactic rules from a source domain are combined with target domain sentences to dynamically generate training data, enhancing the adaptability of the parser to diverse domains.Experiments conducted on textbook and news domains demonstrate the effectiveness of the proposed method, outperforming rule-based baselines by 1.68 points on F1 metrics.