Abstract
In this paper, we introduce an interactive approach to generation of factual questions from unstructured text. Our proposed framework transforms input text into structured set of features and uses them for question generation. Its learning process is based on combination of machine learning techniques known as reinforcement learning and supervised learning. Learning process starts with initial set of pairs formed by declarative sentences and assigned questions and it continuously learns how to transform sentences into questions. Process is also improved by feedback from users regarding already generated questions. We evaluated our approach and the comparison with state-of-the-art systems shows that it is a perspective way for research.
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Acknowledgments
The work reported here was supported by the Scientific Grant Agency of Slovak Republic (VEGA) under the grants No. VG 1/0752/14, VG 1/0646/15, ITMS 26240120039 and STU Grant scheme for Support of Young Researchers.
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Blšták, M., Rozinajová, V. (2017). Machine Learning Approach to the Process of Question Generation. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_12
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DOI: https://doi.org/10.1007/978-3-319-64206-2_12
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