Conversation disentanglement with bi-level contrastive learning

• Main Authors: HUANG, Chengyu, ZHANG, Zheng, FEI, Hao, LIAO, Lizi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Labeled data; Model-based OPC; Multi-party conversations; Public dataset; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7585; https://ink.library.smu.edu.sg/context/sis_research/article/8588/viewcontent/2210.15265.pdf
• Institution: Singapore Management University

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context...