Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, Xiaoyan Zhu


Abstract
Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.
Anthology ID:
D19-1321
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3257–3268
Language:
URL:
https://aclanthology.org/D19-1321
DOI:
10.18653/v1/D19-1321
Bibkey:
Cite (ACL):
Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, and Xiaoyan Zhu. 2019. Long and Diverse Text Generation with Planning-based Hierarchical Variational Model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3257–3268, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (Shao et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/author-url/D19-1321.pdf
Attachment:
 D19-1321.Attachment.zip
Code
 ZhihongShao/Planning-based-Hierarchical-Variational-Model
Data
100DOH