Neural response generation is a typical task in NLP community. DialogWAE is a new approach for dialogue modeling and response generation, which achieves SOTA result on popular datasets. In this work, we focus on exploring the various modalities of the generated responses. To be specific, we propose to: ​

• Analyze how the number K of prior components influences the overall performance​
• Explore what each prior component of the Gaussian mixture distribution captures when K > 3​