A Closer Look at Recent Results of Verb Selection for Data-to-Text NLG

Guanyi Chen, Jin-Ge Yao


Abstract
Automatic natural language generation systems need to use the contextually-appropriate verbs when describing different kinds of facts or events, which has triggered research interest on verb selection for data-to-text generation. In this paper, we discuss a few limitations of the current task settings and the evaluation metrics. We also provide two simple, efficient, interpretable baseline approaches for statistical selection of trend verbs, which give a strong performance on both previously used evaluation metrics and our new evaluation.
Anthology ID:
W19-8622
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
158–163
Language:
URL:
https://www.aclweb.org/anthology/W19-8622
DOI:
10.18653/v1/W19-8622
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-8622.pdf
Supplementary attachment:
 W19-8622.Supplementary_Attachment.pdf