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Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
Learning Causal Effects on Hypergraphs
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes.
Learning to Represent Human Motives for Goal-directed Web Browsing
Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web.
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Although information workers may complain about meetings, they are an essential part of their work life.
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