# Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning

arXiv: https://arxiv.org/abs/2102.11146 · HTML: https://mlsanity.com/paper/2102.11146/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [How to train your MAML](https://mlsanity.com/paper/1810.09502/) (2018)
- [Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://mlsanity.com/paper/1908.10423/) (2019)
- [Domain Adaptive Dialog Generation via Meta Learning](https://mlsanity.com/paper/1906.03520/) (2019)
- [Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation](https://mlsanity.com/paper/1804.08069/) (2018)
- [Zero-Shot Dialog Generation with Cross-Domain Latent Actions](https://mlsanity.com/paper/1805.04803/) (2018)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)
- [Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks](https://mlsanity.com/paper/1910.01302/) (2019)

## Abstract (arXiv)

Current generative-based dialogue systems are data-hungry and fail to adapt to new unseen domains when only a small amount of target data is available. Additionally, in real-world applications, most domains are underrepresented, so there is a need to create a system capable of generalizing to these domains using minimal data. In this paper, we propose a method that adapts to unseen domains by combining both transfer and meta-learning (DATML). DATML improves the previous state-of-the-art dialogue model, DiKTNet, by introducing a different learning technique: meta-learning. We use Reptile, a first-order optimization-based meta-learning algorithm as our improved training method. We evaluated our model on the MultiWOZ dataset and outperformed DiKTNet in both BLEU and Entity F1 scores when the same amount of data is available.