Structure-Preserving Document-Level Data Augmentation via Discourse Parsing

Key points

• Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP).1
• 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.1
• 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.1
• Comparisons with a standard RST parser and controlled ablations further support the proposed discourse representation.1

Sources (1)

• [1]Structure-Preserving Document-Level Data Augmentation via Discourse Parsing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:16 AM

  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.

  Data augmentation (DA) plays a vital role in improving model performance in low-resource or limited-supervision scenarios within Natural Language Processing (NLP).

Extractive summary: sentences quoted from the sources.