  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.
