Multi-domain dialogue state tracking with recursive inference

• Main Authors: LIAO, Lizi, ZHU, Tongyao, LONG, Le Hong, CHUA, Tat-Seng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Dialogue state tracking; Recursive inference; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7582; https://ink.library.smu.edu.sg/context/sis_research/article/8585/viewcontent/Multi_domain_dialogue_state_tracking_with_recursive_inference.pdf
• Institution: Singapore Management University

Multi-domain dialogue state tracking (DST) is a critical component for monitoring user goals during the course of an interaction. Existing approaches have relied on dialogue history indiscriminately or updated on the most recent turns incrementally. However, in spite of modeling it based on fixed ontology or open vocabulary, the former setting violates the interactive and progressing nature of dialogue, while the later easily gets affected by the error accumulation conundrum. Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Specifically, our agent reversely reviews the dialogue history until the agent has pinpointed sufficient turns confidently for slot value prediction. It also recursively factors in potential dependencies among domains and slots to further solve the co-reference and value sharing problems. The quantitative and qualitative experimental results on the MultiWOZ 2.1 corpus demonstrate that the proposed ReInf not only outperforms the state-of-the-art methods, but also achieves reasonable turn reference and interpretable slot co-reference.