  Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented
chatbots, help users achieve a predefined goal (e.g. book a movie ticket)
within a closed domain. A first step is to understand the user's goal by using
natural language understanding techniques. Once the goal is known, the bot must
manage a dialogue to achieve that goal, which is conducted with respect to a
learnt policy. The success of the dialogue system depends on the quality of the
policy, which is in turn reliant on the availability of high-quality training
data for the policy learning method, for instance Deep Reinforcement Learning.
  Due to the domain specificity, the amount of available data is typically too
low to allow the training of good dialogue policies. In this master thesis we
introduce a transfer learning method to mitigate the effects of the low
in-domain data availability. Our transfer learning based approach improves the
bot's success rate by $20\%$ in relative terms for distant domains and we more
than double it for close domains, compared to the model without transfer
learning. Moreover, the transfer learning chatbots learn the policy up to 5 to
10 times faster. Finally, as the transfer learning approach is complementary to
additional processing such as warm-starting, we show that their joint
application gives the best outcomes.
