Knowledge Graph-Based Reinforcement Federated Learning for Chinese Question and Answering

Abstract

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model MSP-bidirectional and auto-regressive transformers (BART) that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.