35 citations · 149 across the 13 of their papers we have counts for
6 papers · 1 filter
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…