A DOMAIN-ADAPTIVE QUESTION ANSWERING FRAMEWORK FOR FINANCIAL TEXTS WITH MULTI-TASK SEMANTIC REASONING

Abstract

The rapid expansion of financial markets and the increasing complexity of financial instruments have created an urgent need for intelligent systems capable of understanding and responding to domain-specific inquiries. Traditional question answering (QA) models often fail to generalize beyond narrow datasets and exhibit poor comprehension of financial terminology, leading to suboptimal performance in real-world financial contexts. To address these limitations, this study introduces a domain-adaptive financial QA framework that integrates a financial knowledge graph with a multi-task semantic reasoning mechanism based on the T5 transformer architecture.The proposed system leverages three interconnected sub-tasks—named entity recognition (NER), entity linking, and domain-specific answer generation—trained collaboratively on a curated dataset titled “FinQ-Fuse,” which consists of over 120,000 annotated financial QA pairs. The NER module utilizes a BiLSTM-CRF architecture, while entity linking combines rule-based filtering with a support vector machine (SVM) classifier. Domain-specific question answering is powered by a fine-tuned T5 model that incorporates contextual and graph-based knowledge.Experimental evaluations conducted on benchmark datasets (FiQA, FinQA, and a custom Chinese financial QA dataset) demonstrate that the proposed framework achieves substantial improvements over baseline models. The system records an accuracy of 88%, a BLEU score of 0.72, and an entity coverage rate of 78%, indicating its superior performance in both linguistic and knowledge-aware tasks.This research validates the effectiveness of integrating semantic reasoning with structured domain knowledge for financial QA systems. By significantly enhancing answer quality, contextual awareness, and entity understanding, the proposed model sets a new benchmark for intelligent financial information retrieval. The findings hold strong practical implications for investment analysis, customer service automation, and financial education, paving the way for more adaptive and explainable AI systems in the financial sector..