Culture-Aware Dialogue Management for Conversational Assistants

The cultural background has a great influence on the people’s behaviour and perception. With the aim of designing a culturally sensitive conversational assistant, we have investigated whether culture-specific parameters may be trained by use of a supervised learning approach. We have used a dialogue management framework based on the concept of probabilistic rules and a multicultural data set to generate a culture-aware dialogue manager which allows communication in accordance with the user’s cultural idiosyncrasies. Hence, the system response to a user action varies depending on the user’s culture. Our data set contains 258 spoken dialogues from four different European cultures: German, Polish, Spanish and Turkish. For our evaluation, we have trained a culture-specific dialogue domain for each culture. Afterwards, we have compared the probability distributions of the parameters which are responsible for the selection of the next system action. The evaluation results show that culture-specific parameters have been trained and thus represent cultural patterns in the dialogue management decision process.