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20172021
most citedNeural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration

9 citations · 17 across the 11 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG20212 cited

Learning Slice-Aware Representations with Mixture of Attentions

Cheng Wang, Sungjin Lee, Sunghyun Park +3

Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score. However, model improvement and deve…

cs.LG2021

Handling Long-Tail Queries with Slice-Aware Conversational Systems

Cheng Wang, Sun Kim, Taiwoo Park +5

We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives. These systems normally rely on machine learning mode…

cs.LG2020

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.LG2019

Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding

Jihwan Lee, Ruhi Sarikaya, Young-Bum Kim

In this paper, we introduce an approach for leveraging available data across multiple locales sharing the same language to 1) improve domain classification model accuracy in Spoken…

cs.LG2019

Continuous Learning for Large-scale Personalized Domain Classification

Han Li, Jihwan Lee, Sidharth Mudgal +2

Domain classification is the task of mapping spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants (IPDAs). Thi…