# Response Generation by Context-aware Prototype Editing

arXiv: https://arxiv.org/abs/1806.07042 · HTML: https://mlsanity.com/paper/1806.07042/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 30 of 40)

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- [Goldfish: Vision-Language Understanding of Arbitrarily Long Videos](https://mlsanity.com/paper/2407.12679/) (2024)
- [What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://mlsanity.com/paper/2105.00816/) (2021)
- [Copy Is All You Need](https://mlsanity.com/paper/2307.06962/) (2023)
- [Personalized Large Language Model Assistant with Evolving Conditional Memory](https://mlsanity.com/paper/2312.17257/) (2023)
- [RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling](https://mlsanity.com/paper/2105.06597/) (2021)
- [Few-Shot Table-to-Text Generation with Prototype Memory](https://mlsanity.com/paper/2108.12516/) (2021)
- [Accelerating Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.15246/) (2024)
- [Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation](https://mlsanity.com/paper/2109.05487/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Transferable Persona-Grounded Dialogues via Grounded Minimal Edits](https://mlsanity.com/paper/2109.07713/) (2021)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [Retrieval is Accurate Generation](https://mlsanity.com/paper/2402.17532/) (2024)
- [TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge](https://mlsanity.com/paper/2203.08517/) (2022)
- [Controlling Text Edition by Changing Answers of Specific Questions](https://mlsanity.com/paper/2105.11018/) (2021)
- [EmpBot: A T5-based Empathetic Chatbot focusing on Sentiments](https://mlsanity.com/paper/2111.00310/) (2021)
- [Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://mlsanity.com/paper/2404.02505/) (2024)
- [Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation](https://mlsanity.com/paper/2108.12582/) (2021)
- [Hierarchical Context Tagging for Utterance Rewriting](https://mlsanity.com/paper/2206.11218/) (2022)
- [Understanding and Improving the Exemplar-based Generation for Open-domain Conversation](https://mlsanity.com/paper/2112.06723/) (2021)
- [Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation](https://mlsanity.com/paper/2201.11367/) (2022)
- [Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization](https://mlsanity.com/paper/2302.04415/) (2023)
- [Adapting Knowledge for Few-shot Table-to-Text Generation](https://mlsanity.com/paper/2302.12468/) (2023)
- [Affective Feedback Synthesis Towards Multimodal Text and Image Data](https://mlsanity.com/paper/2203.12692/) (2022)

## Abstract (arXiv)

Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.