Learning culturally situated dialogue strategies to support language learners

• Main Authors: Victoria Abou-Khalil (Author), Toru Ishida (Author), Masayuki Otani (Author), Brendan Flanagan (Author), Hiroaki Ogata (Author), Donghui Lin (Author)
• Format: Book
• Published: The Asia-Pacific Society for Computers in Education (APSCE), 2018-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s41039-018-0076-x; 1793-7078
• Keywords: Language learning; Intercultural competence; Automatic dialogue strategies; Reinforcement learning; Wizard of Oz; Culturally situated information; Information technology; T58.5-58.64; Education; L
• Source: Research and Practice in Technology Enhanced Learning, Vol 13, Iss 1, Pp 1-20 (2018)

Abstract Successful language learning requires an understanding of the target culture in order to make valuable usage of the learned language. To understand a foreign culture, language students need the knowledge of its related products, as well as the skill of comparing them to those of their own culture. One way for students to understand foreign products is by making Culturally Situated Associations (CSA), i.e., relating the products they encountered to products from their own culture. In order to provide students with CSA that they can understand, we must gather information about their culture, provide them with the CSA, and make sure they understand it. In this case, a Culturally Situated Dialogue (CSD) must take place. To carry the dialogue, dialogue systems must follow a dialogue strategy. However, previous work showed that handcrafted dialogue strategies were shown to be ineffective in comparison with machine-learned dialogue strategies. In this research, we proposed a method to learn CSD strategies to support foreign students, using a reinforcement learning algorithm. Since no previous system providing CSA was implemented, the method allowed the creation of CSD strategies when no initial data or prototype exists. The method was applied to generate three different agents: the novice agent was based on an eight states feature-space, the intermediate agent was based on a 144 states feature-space, and the advanced agent was based on a 288 states feature-space. Each of these agents learned a different dialogue strategy. We conducted a Wizard of Oz experiment during which, the agents' role was to support the wizard in their dialogue with students by providing them with the appropriate action to take at each step. The resulting dialogue strategies were evaluated based on the quality of the strategy. The results suggest the use of the novice agent at the first stages of prototyping the dialogue system. The intermediate agent and the advanced agent could be used at later stages of the system's implementation.