Controllable Neural Natural Language Generation: comparison of state-of-the-art control strategies

Yuanmin Leng, François Portet, Cyril Labbé, Raheel Qader

Abstract
Most NLG systems target text fluency and grammatical correctness, disregarding control over text structure and length. However, control over the output plays an important part in industrial NLG applications. In this paper, we study different strategies of control in triple-totext generation systems particularly from the aspects of text structure and text length. Regarding text structure, we present an approach that relies on aligning the input entities with the facts in the target side. It makes sure that the order and the distribution of entities in both the input and the text are the same. As for control over text length, we show two different approaches. One is to supply length constraint as input while the other is to force the end-ofsentence tag to be included at each step when using top-k decoding strategy. Finally, we propose four metrics to assess the degree to which these methods will affect a NLG system’s ability to control text structure and length. Our analyses demonstrate that all the methods enhance the system’s ability with a slight decrease in text fluency. In addition, constraining length at the input level performs much better than control at decoding level.
Anthology ID:
2020.webnlg-1.4
Volume:
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Month:
12
Year:
2020
Address:
Dublin, Ireland (Virtual)
Editors:
Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
Venue:
WebNLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
34–39
Language:
URL:
https://aclanthology.org/2020.webnlg-1.4
DOI:
Bibkey:
Cite (ACL):
Yuanmin Leng, François Portet, Cyril Labbé, and Raheel Qader. 2020. Controllable Neural Natural Language Generation: comparison of state-of-the-art control strategies. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 34–39, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
Controllable Neural Natural Language Generation: comparison of state-of-the-art control strategies (Leng et al., WebNLG 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/landing_page/2020.webnlg-1.4.pdf
PDF Search