Chi, Ta-ChungTa-ChungChiChen, Po-ChunPo-ChunChenSu, Shang-YuShang-YuSuYUN-NUNG CHEN2025-12-082025-12-082017https://www.scopus.com/record/display.uri?eid=2-s2.0-105019520968&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/734338Language understanding (LU) and dialogue policy learning are two essential components in conversational systems. Human-human dialogues are not well-controlled and often random and unpredictable due to their own goals and speaking habits. This paper proposes a role-based contextual model to consider different speaker roles independently based on the various speaking patterns in the multi-turn dialogues. The experiments on the benchmark dataset show that the proposed role-based model successfully learns role-specific behavioral patterns for contextual encoding and then significantly improves language understanding and dialogue policy learning tasksconference paper2-s2.0-105019520968