Abstract
As the importance of learning foreign languages increases, the demand for language learning materials is increasing. A dialog system can be one of useful multimedia resources for language learning. Contrary to information-seeking dialog systems, ranking user/system dialog acts can be very useful for language tutoring purpose. Therefore we propose a novel and general method to rank dialog acts that looks up dialog histories using an enhanced Levenshtein distance based on the impact of each dialog act on the dialog history. Experimental results showed that the proposed model outperformed the baseline system in ranking performance. Also the proposed method exhibited a better task completion rate than the baseline system, implicating the use of the proposed method as a new model for general dialog management. Furthermore, the proposed method could generate more diverse system responses than the baseline system, which is very desirable for the language tutoring purpose.
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Acknowledgements
This work was supported by the Industrial Strategic technology development program, 10035252, development of dialog-based spontaneous speech interface technology on mobile platform, funded by the Ministry of Knowledge Economy (MKE, Korea), and was performed for the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy (MKE)
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Noh, H., Lee, S., Kim, K., Lee, K., Lee, G.G. (2011). Ranking Dialog Acts using Discourse Coherence Indicator for Language Tutoring Dialog Systems. In: Delgado, RC., Kobayashi, T. (eds) Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1335-6_21
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DOI: https://doi.org/10.1007/978-1-4614-1335-6_21
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