53 citations · 78 across the 19 of their papers we have counts for
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cs.CL2020
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Sunghyun Park, Han Li, Ameen Patel +5
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding…
cs.LG2020★ 3 cited
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents
Mohammad Kachuee, Hao Yuan, Young-Bum Kim +1
Turn-level user satisfaction is one of the most important performance metrics for conversational agents. It can be used to monitor the agent's performance and provide insights abou…
cs.HC2020★ 3 cited
Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents
Dookun Park, Hao Yuan, Dongmin Kim +10
Measuring user satisfaction level is a challenging task, and a critical component in developing large-scale conversational agent systems serving the needs of real users. An widely…