Improving Language Learning Performance Using Multimodal Dialogue Systems
- DOI
- 10.2991/978-94-6463-242-2_23How to use a DOI?
- Keywords
- language learning; natural language processing; multimodal interaction; multimodal dialogue system
- Abstract
Learning a language poses a significant challenge for learners, who must comprehend the intricacies of language and analyze the relationships among its components. While computer technologies have been developed to assist language learning, they fail to mirror the human cognitive process. This paper examines the application of a multimodal dialogue system to enhance language learning outcomes. The system boasts several advantages. Firstly, smart devices can collect multimodal data in learning environments to monitor the learner’s status in real-time, thus enhancing the accuracy of intention recognition. Secondly, the system can interact with learners naturally by analyzing their multimodal data, resulting in improved language skills. Finally, application scenarios are designed based on the defined multimodal dialogue system, which effectively demonstrates the system’s ability to enhance language learning performance.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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.
Cite this article
TY - CONF AU - Zhenyu Wu AU - Zhiyang Ding AU - Zhaowei Zhang AU - Yanqin Mao PY - 2023 DA - 2023/09/22 TI - Improving Language Learning Performance Using Multimodal Dialogue Systems BT - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) PB - Atlantis Press SP - 191 EP - 197 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-242-2_23 DO - 10.2991/978-94-6463-242-2_23 ID - Wu2023 ER -