HCN-RLR-CAN: A novel human-computer negotiation model based on round-level recurrence and causal attention networks

Abstract

Recent advances in Human-Computer Negotiation (HCN) systems have shown promising progress in simulating complex negotiation dialogues. However, these systems still need to grapple with critical challenges such as limited adaptability to proper negotiation tones, ineffective capture of long-term dependencies, and constraints in generating diverse, natural, and strategically sound responses. To address these limitations, we propose the HCN-RLR-CAN, a novel HCN model based on Round-Level Recurrence (RLR) and Causal Attention Network (CAN). Our approach, which uniquely processes dialogues as role-based parallel texts and employs a hierarchical encoder to comprehend user dialogue history, offers a fresh perspective on addressing these challenges. The model employs causal attention learning modules to separately model linguistic strategies and dialogue acts. Additionally, it employs a round-level recursive decoding mechanism that generates responses by synthesising historical dialogue information, dialogue act and strategy encodings, and previous decoding states. With its significant performance advantages over baseline models, the HCN-RLR-CAN model has the potential to inspire a new wave of research and development in the field of HCN systems.