# A Conditional Variational Framework for Dialog Generation

arXiv: https://arxiv.org/abs/1705.00316 · HTML: https://mlsanity.com/paper/1705.00316/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2017.

## Built on by (most cited first, 16 of 16)

- [Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders](https://mlsanity.com/paper/2106.03410/) (2021)
- [Modulating Language Models with Emotions](https://mlsanity.com/paper/2108.07886/) (2021)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables](https://mlsanity.com/paper/2212.01145/) (2022)
- [An Empirical Bayes Framework for Open-Domain Dialogue Generation](https://mlsanity.com/paper/2311.10945/) (2023)
- [Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems](https://mlsanity.com/paper/2105.00079/) (2021)
- [Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs](https://mlsanity.com/paper/2506.15131/) (2025)
- [A Conditional Generative Matching Model for Multi-lingual Reply Suggestion](https://mlsanity.com/paper/2109.07046/) (2021)
- [CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models](https://mlsanity.com/paper/2205.10558/) (2022)
- [Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder](https://mlsanity.com/paper/2212.00231/) (2022)
- [Frugal Prompting for Dialog Models](https://mlsanity.com/paper/2305.14919/) (2023)
- [Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems](https://mlsanity.com/paper/2507.05940/) (2025)
- [Android Security using NLP Techniques: A Review](https://mlsanity.com/paper/2107.03072/) (2021)
- [Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation](https://mlsanity.com/paper/2305.15025/) (2023)
- [A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation](https://mlsanity.com/paper/2410.20766/) (2024)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)

## Abstract (arXiv)

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.