24 citations · 66 across the 16 of their papers we have counts for
16 papers
Human-AI Interactions Through A Gricean Lens
Laura Panfili, Steve Duman, Andrew Nave +3
Grice's Cooperative Principle (1975) describes the implicit maxims that guide conversation between humans. As humans begin to interact with non-human dialogue systems more frequent…
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)…
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…
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…
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents
Pragaash Ponnusamy, Alireza Roshan Ghias, Chenlei Guo +1
Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Ty…
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…
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…
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding
JIhwan Lee, Dongchan Kim, Ruhi Sarikaya +1
Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new mo…
Differentiable Greedy Networks
Thomas Powers, Rasool Fakoor, Siamak Shakeri +4
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is t…
Direct optimization of F-measure for retrieval-based personal question answering
Rasool Fakoor, Amanjit Kainth, Siamak Shakeri +3
Recent advances in spoken language technologies and the introduction of many customer facing products, have given rise to a wide customer reliance on smart personal assistants for…
Contextual Slot Carryover for Disparate Schemas
Chetan Naik, Arpit Gupta, Hancheng Ge +2
In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring…
Efficient Large-Scale Domain Classification with Personalized Attention
Young-Bum Kim, Dongchan Kim, Anjishnu Kumar +1
In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (I…
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding
Young-Bum Kim, Dongchan Kim, Joo-Kyung Kim +1
Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping dom…
Speaker-Sensitive Dual Memory Networks for Multi-Turn Slot Tagging
Young-Bum Kim, Sungjin Lee, Ruhi Sarikaya
In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neur…