Abstract
Question generation and question answering are attracting more and more attention recently. Existing question generation systems produce questions based on the given text. However, there is still a vast gap between these generated questions and their practical usage, which acquires more modification from human beings. In order to alleviate this dilemma, we consider reducing the volume of the question set/suite and extracting a lightweight subset while conserving as many features as possible from the original set. In this paper, we first propose a three-layer semantic analysis model, which ensembles traditional language analysis tools to perform the reduction. Then, a bunch of metrics over semantic contribution is carefully designed to balance distinct features. Finally, we introduce the concept of Grade Level and Information Entropy to evaluate our model from a multi-dimensional manner. We conduct an extensive set of experiments to test our model for question suite reduction. The results demonstrate that it can retain as much diversity as possible compared to the original large set.
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Acknowledgement
The work is supported in part by the National Key Research and Development Program of China (2016YFC0800805) and the National Natural Science Foundation of China (61772014).
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Dai, W., Sheni, S., Hei, T. (2019). 3Q: A 3-Layer Semantic Analysis Model for Question Suite Reduction. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_18
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DOI: https://doi.org/10.1007/978-3-030-36204-1_18
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