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Goal-Oriented (GO) Dialogue Systems, colloquially known as goal orientedchatbots, 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 usingnatural language understanding techniques. Once the goal is known, the bot mustmanage a dialogue to achieve that goal, which is conducted with respect to alearnt policy. The success of the dialogue system depends on the quality of thepolicy, which is in turn reliant on the availability of high-quality trainingdata for the policy learning method, for instance Deep Reinforcement Learning.  Due to the domain specificity, the amount of available data is typically toolow to allow the training of good dialogue policies. In this paper we introducea transfer learning method to mitigate the effects of the low in-domain dataavailability. Our transfer learning based approach improves the bot's successrate by 20% in relative terms for distant domains and we more than double itfor 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 additionalprocessing such as warm-starting, we show that their joint application givesthe best outcomes.

Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning