Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases

Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, Huawei Shen, Xueqi Cheng


Abstract
Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints. Existing methods train one encoder-decoder-based model to fit all questions. However, such a one-size-fits-all strategy may not perform well since complex questions exhibit an uneven distribution in many dimensions, such as question types, involved KB relations, and query structures, resulting in insufficient learning for long-tailed samples under different dimensions. To address this problem, we propose a meta-learning framework for complex question generation. The meta-trained generator can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples through a few most related training samples. To retrieve similar samples for each input query, we design a self-supervised graph retriever to learn distributed representations for samples, and contrastive learning is leveraged to improve the learned representations. We conduct experiments on both WebQuestionsSP and ComplexWebQuestion, and results on long-tailed samples of different dimensions have been significantly improved, which demonstrates the effectiveness of the proposed framework.
Anthology ID:
2022.coling-1.533
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6105–6114
Language:
URL:
https://aclanthology.org/2022.coling-1.533
DOI:
Bibkey:
Cite (ACL):
Kun Zhang, Yunqi Qiu, Yuanzhuo Wang, Long Bai, Wei Li, Xuhui Jiang, Huawei Shen, and Xueqi Cheng. 2022. Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6105–6114, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (Zhang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.533.pdf
Data
WebQuestionsSP