9 citations · 35 across the 19 of their papers we have counts for
19 papers
Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding
Joo-Kyung Kim, Guoyin Wang, Sungjin Lee +1
A large-scale conversational agent can suffer from understanding user utterances with various ambiguities such as ASR ambiguity, intent ambiguity, and hypothesis ambiguity. When am…
AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation
Xinnuo Xu, Guoyin Wang, Young-Bum Kim +1
Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For larg…
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…
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…
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Han Li, Sunghyun Park, Aswarth Dara +5
Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR)…
DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues
Ziming Li, Dookun Park, Julia Kiseleva +2
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn. However, evaluating multi…