DeepAI AI Chat
Log In Sign Up

Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning

by   Rui Ribeiro, et al.

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.


page 1

page 2

page 3

page 4


Meta-Learning for Few-Shot NMT Adaptation

We present META-MT, a meta-learning approach to adapt Neural Machine Tra...

Few Shot Dialogue State Tracking using Meta-learning

Dialogue State Tracking (DST) forms a core component of automated chatbo...

Meta Dialogue Policy Learning

Dialog policy determines the next-step actions for agents and hence is c...

Personalizing Dialogue Agents via Meta-Learning

Existing personalized dialogue models use human designed persona descrip...

Domain Adaptive Dialog Generation via Meta Learning

Domain adaptation is an essential task in dialog system building because...

Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

Learning with minimal data is one of the key challenges in the developme...

A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting

Numerous new dialog domains are being created every day while collecting...