期刊论文

Zhou, GY

英文

ACM Transactions on Asian and Low-Resource Language Information Processing

2375-4699

2022

21

2

10.1145/3474555

This work was supported by the National Natural Science Foundation of China under Grant 61972173, and also supported in part by the research grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada and York Research Chairs (YRC) program. Author’s addresses: Y. Hu, H. Yang, and G. Zhou (corresponding author), School of Computer, Central China Normal University, Hongshan District, Wuhan, 430079, China; emails: huyue2017@mails.ccnu.edu.cn, {htyang, gyzhou}@mail. ccnu.edu.cn; J. X. Huang, School of Information Technology, York University, 4700 Keele Street Toronto, Ontario, Canada; email: jhuang@yorku.ca. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 2375-4699/2022/01-ART31 $15.00 https://doi.org/10.1145/3474555

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Question generation over knowledge bases is an important research topic. How to deal with rare and low-frequency words in traditional generation models is a key challenge for question generation. Although the copy mechanism provides significant performance improvements, the original copy mechanism weakens the focus on aspect generation in the overall representations. In this article, we present a novel method to improve question generation with a question type enhanced representation and attention-based copy mechanism. The proposed method explo...