---
abstract: 'We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.'
author:
- |
    Cao Liu$^{1,2}$, Kang Liu$^{1,2}$, Shizhu He$^{1,2}$, Zaiqing Nie$^3$, Jun Zhao$^{1,2}$\
    $^1$ National Laboratory of Pattern Recognition, Institute of Automation,\
    Chinese Academy of Sciences, Beijing, 100190, China\
    $^2$ University of Chinese Academy of Sciences, Beijing, 100049, China\
    $^3$ Alibaba AI Labs, Beijing, 100029, China\
    {cao.liu, kliu, shizhu.he, jzhao}@nlpr.ia.ac.cn\
    zaiqing.nzq@alibaba-inc.com
bibliography:
- 'emnlp-2019.bib'
title: |
    Generating Questions for Knowledge Bases\
    via Incorporating Diversified Contexts and Answer-Aware Loss
---

Introduction {#Setion: Introduction}
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Question Generation over Knowledge Bases (KBQG) aims at generating natural language questions for the corresponding 