Triple-Joint Modeling for Question Generation Using Cross-Task Autoencoder.
NLPCC (2)(2019)
摘要
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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关键词
Question Generation, Autoencoder, Joint learning
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