Abstract
Question Generation (QG) aims to generate a question based on the context. Given the intrinsic connections between QG and QA (Question Answering), we focus on training a joint model for both QG and QA, and take one step further to integrate one more context self-encoding (CSE) task into the joint model, as a junction auxiliary task to better integrate QG and QA. In particular, our model employs a cross-task autoencoder to incorporate QG, QA and CSE into a joint learning process, which could better utilize the correlation between the contexts of different tasks in learning representations and provide more task-specific information. Experimental results show the effectiveness of our triple-task training model for QG, and the importance of learning interaction among QA and CSE for QG.
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Acknowledgments
This research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), and the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010).
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Wang, H., Wu, R., Li, Z., Wang, Z., Chen, Z., Zhou, G. (2019). Triple-Joint Modeling for Question Generation Using Cross-Task Autoencoder. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_26
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DOI: https://doi.org/10.1007/978-3-030-32236-6_26
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