Abstract

"Frame Semantic Parsing (FSP) is a critical task in natural language processing (NLP) that involves identifying semantic frames, argument spans, and their corresponding roles within a sentence. This paper presents a novel approach to Chinese Frame Seman-tic Parsing by fine-tuning the Qwen3 large language model to simultaneously address three sub-tasks: Frame Identification, Argument Identification, and Role Identification.We propose a unified prompt-based framework with iterative refinements, including direct argument output for span identification and a majority-voting mechanism for frame prediction. Our experiments demonstrate significant improvements in argument and role identification through modified output formats, while frame identification benefits from ensemble voting. However, integrating Chain-of-Thought (CoT) reasoning with model-generated explanations yielded suboptimal results, suggesting limitations in the auxiliary model’s performance. This work highlights the potential of fine-tuned large language models for complex semantic parsing tasks and identifies avenues for further optimization."