35 citations · 149 across the 13 of their papers we have counts for
14 papers
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…
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks
Igor Shalyminov, Sungjin Lee, Arash Eshghi +1
Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-drive…
Structuring Latent Spaces for Stylized Response Generation
Xiang Gao, Yizhe Zhang, Sungjin Lee +4
Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate response…
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
Igor Shalyminov, Sungjin Lee, Arash Eshghi +1
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where d…
ConvLab: Multi-Domain End-to-End Dialog System Platform
Sungjin Lee, Qi Zhu, Ryuichi Takanobu +8
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a lar…
Jointly Optimizing Diversity and Relevance in Neural Response Generation
Xiang Gao, Sungjin Lee, Yizhe Zhang +4
Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the…
Consistent Dialogue Generation with Self-supervised Feature Learning
Yizhe Zhang, Xiang Gao, Sungjin Lee +4
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversat…
Nudging Neural Conversational Model with Domain Knowledge
Sungjin Lee
Neural conversation models are attractive because one can train a model directly on dialog examples with minimal labeling. With a small amount of data, however, they often fail to…
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Sungjin Lee, Rahul Jha
Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled da…
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
Young-Bum Kim, Sungjin Lee, Karl Stratos
In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according t…
Toward Continual Learning for Conversational Agents
Sungjin Lee
While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they…
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…
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li +4
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example,…