# Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

arXiv: https://arxiv.org/abs/1910.14326 · HTML: https://mlsanity.com/paper/1910.14326/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 15 of 15)

- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation](https://mlsanity.com/paper/2310.08395/) (2023)
- [Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation](https://mlsanity.com/paper/2207.03509/) (2022)
- [kFolden: k-Fold Ensemble for Out-Of-Distribution Detection](https://mlsanity.com/paper/2108.12731/) (2021)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [Generating Personalized Dialogue via Multi-Task Meta-Learning](https://mlsanity.com/paper/2108.03377/) (2021)
- [Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation](https://mlsanity.com/paper/2203.11670/) (2022)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting](https://mlsanity.com/paper/2104.02689/) (2021)
- [Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks](https://mlsanity.com/paper/2105.10323/) (2021)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Reinforcement Learning for Few-Shot Text Generation Adaptation](https://mlsanity.com/paper/2111.11030/) (2021)
- [Classical Sequence Match is a Competitive Few-Shot One-Class Learner](https://mlsanity.com/paper/2209.06394/) (2022)
- [A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation](https://mlsanity.com/paper/2410.20174/) (2024)
- [Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM](https://mlsanity.com/paper/2605.01973/) (2026)

## Abstract (arXiv)

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning framework which pre-trains the parameters on all non-target tasks then fine-tunes on the target task. However, fine-tuning distinguishes tasks from the parameter perspective but ignores the model-structure perspective, resulting in similar dialogue models for different tasks. In this paper, we propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. In our approach, each dialogue model consists of a shared module, a gating module, and a private module. The first two modules are shared among all the tasks, while the third one will differentiate into different network structures to better capture the characteristics of the corresponding task. The extensive experiments on two datasets show that our method outperforms all the baselines in terms of task consistency, response quality, and diversity.