# A Hybrid Retrieval-Generation Neural Conversation Model

arXiv: https://arxiv.org/abs/1904.09068 · HTML: https://mlsanity.com/paper/1904.09068/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 18 of 18)

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- [RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling](https://mlsanity.com/paper/2105.06597/) (2021)
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- [Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://mlsanity.com/paper/2307.16013/) (2023)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
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- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots](https://mlsanity.com/paper/2301.12400/) (2023)
- [STRIVE: A Think & Improve Approach with Iterative Refinement for Enhancing Question Quality Estimation](https://mlsanity.com/paper/2504.05693/) (2025)
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## Abstract (arXiv)

Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.