arXiv：cs.CL· Ruilin Shang, Shilong Hou, Zi Long, Xianghua Fu·· 16 小时前
基于篇章解析的结构保持文档级数据增强框架
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing
AI 导读
研究团队提出一种基于 LLM 与文档级篇章结构解析的结构保持数据增强框架，通过识别句对间的修辞关系并提取关键短语，在保持原有篇章结构的前提下替换为语义连贯但主题偏移的内容。在文本摘要和问答任务上，增强数据提供了互补监督，改善了联合数据训练效果；与标准 RST 解析器的对比和消融实验进一步验证了该篇章表示的有效性。
正文
Abstract:Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP). Existing DA methods, such as synonym replacement and back-translation, have demonstrated effectiveness on the word or sentence level; however, they frequently neglect discourse-level coherence and logical flow, which are essential for complex tasks dependent on inter-sentential relationships. In this paper, we propose a structure-preserving document-level data augmentation framework based on large language models (LLMs) and document-level discourse structure parsing. Our approach identifies rhetorical relations between sentence pairs and extracts key phrases, which are then replaced with semantically coherent but topically shifted content while preserving the original discourse structure. Experimental results on text summarization and question answering show that the proposed augmented data provides complementary supervision and improves combined-data training across the evaluated tasks. Comparisons with a standard RST parser and controlled ablations further support the proposed discourse representation.
| Comments: | 11 pages, 1 figure, 6 tables. Accepted at the 40th Pacific Asia Conference on Language, Information and Computation (PACLIC 2026) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.19497 [cs.CL] |
| (or arXiv:2610.19497v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19497 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shang Ruilin [view email]
[v1]
Wed, 14 Oct 2026 03:16:07 UTC (117 KB)
来源：arXiv：cs.CL · arxiv.org