Natural Language Decomposition

Natural language decomposition focuses on breaking down complex text into smaller, more manageable units to improve understanding and reasoning. Current research explores various decomposition strategies, from sentence-level segmentation to finer-grained sub-sentence analysis, often leveraging large language models and neuro-symbolic approaches to interpret these units and their relationships. This work aims to enhance tasks like textual entailment, factuality scoring, and natural language interface design by explicitly modeling implicit content and handling complex user requests more effectively. The resulting improvements in text representation and reasoning have significant implications for various NLP applications and social science research.