Abstract
Numerous machine learning tasks achieved substantial advances with the help of large-scale supervised learning corpora over past decade. However, there’s no large-scale question-answer corpora available for Chinese question answering over knowledge bases. In this paper, we present a 28M Chinese Q&A corpora based on the Chinese knowledge base provided by NLPCC2017 KBQA challenge. We propose a novel neural network architecture which combines template-based method and seq2seq learning to generate highly fluent and diverse questions. Both automatic and human evaluation results show that our model achieves outstanding performance (76.8 BLEU and 43.1 ROUGE). We also propose a new statistical metric called DIVERSE to measure the linguistic diversity of generated questions and prove that our model can generate much more diverse questions compared with other baselines.
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We will release the dataset in the future.
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Acknowledgments
We thank the anonymous reviewers for their valuable comments. This work is supported by the National Key Basic Research Program of China (No. 2014CB340504) and the National Natural Science Foundation of China (No. 61375074, 61273318). The contact authors are Zhifang Sui and Baobao Chang.
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Liu, T., Wei, B., Chang, B., Sui, Z. (2018). Large-Scale Simple Question Generation by Template-Based Seq2seq Learning. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_7
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