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Title: Learning culturally situated dialogue strategies to support language learners
Authors: Victoria, Abou-Khalil
Ishida, Toru
Otani, Masayuki
Flanagan, Brendan  kyouindb  KAKEN_id  orcid (unconfirmed)
Ogata, Hiroaki  kyouindb  KAKEN_id  orcid (unconfirmed)
Lin, Donghui  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 緒方, 広明
Keywords: Language learning
Intercultural competence
Automatic dialogue strategies
Reinforcement learning
Wizard of Oz
Culturally situated information
Issue Date: 25-Jul-2018
Publisher: Springer Nature
Journal title: Research and Practice in Technology Enhanced Learning
Volume: 13
Thesis number: 10
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.
Rights: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
DOI(Published Version): 10.1186/s41039-018-0076-x
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