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    <abstract>The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of high-quality comprehensive descriptions for tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness to generate more informative as well as more loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO dataset as a benchmark. Besides, we create WB-filter based on WIKIBIO to test our model in the simulated user-oriented scenarios, in which the generated descriptions should accord with particular user interests. Experimental results show that our model outperforms the state-of-the-art baselines on both automatic and human evaluation.</abstract>
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%0 Conference Proceedings
%T Towards Comprehensive Description Generation from Factual Attribute-value Tables
%A Liu, Tianyu
%A Luo, Fuli
%A Yang, Pengcheng
%A Wu, Wei
%A Chang, Baobao
%A Sui, Zhifang
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-towards-comprehensive
%X The comprehensive descriptions for factual attribute-value tables, which should be accurate, informative and loyal, can be very helpful for end users to understand the structured data in this form. However previous neural generators might suffer from key attributes missing, less informative and groundless information problems, which impede the generation of high-quality comprehensive descriptions for tables. To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing. Furthermore, we propose reinforcement learning for information richness to generate more informative as well as more loyal descriptions for tables. In our experiments, we utilize the widely used WIKIBIO dataset as a benchmark. Besides, we create WB-filter based on WIKIBIO to test our model in the simulated user-oriented scenarios, in which the generated descriptions should accord with particular user interests. Experimental results show that our model outperforms the state-of-the-art baselines on both automatic and human evaluation.
%R 10.18653/v1/P19-1600
%U https://aclanthology.org/P19-1600
%U https://doi.org/10.18653/v1/P19-1600
%P 5985-5996

Markdown (Informal)

[Towards Comprehensive Description Generation from Factual Attribute-value Tables](https://aclanthology.org/P19-1600) (Liu et al., ACL 2019)

• Towards Comprehensive Description Generation from Factual Attribute-value Tables (Liu et al., ACL 2019)

ACL

• Tianyu Liu, Fuli Luo, Pengcheng Yang, Wei Wu, Baobao Chang, and Zhifang Sui. 2019. Towards Comprehensive Description Generation from Factual Attribute-value Tables. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5985–5996, Florence, Italy. Association for Computational Linguistics.