Studying the Impact of Filling Information Gaps on the Output Quality of Neural Data-to-Text

Craig Alexander Thomson, Zhijie Zhao, Somayajulu Gowri Sripada

Research output: Contribution to conferenceUnpublished paperpeer-review

3 Citations (Scopus)
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It is unfair to expect neural data-to-text to produce high quality output when there are gaps between system input data and information contained in the training text. Thomson et al. (2020) identify and narrow information gaps in Rotowire, a popular data-to-text dataset. In this paper, we describe a study which finds that a state-of-the-art neural data-to-text system produces higher quality output, according
to the information extraction (IE) based metrics, when additional input data is carefully selected from this newly available source. It remains to be shown, however, whether IE metrics used in this study correlate well with humans in judging text quality
Original languageEnglish
Number of pages6
Publication statusPublished - Dec 2020
EventProceedings of the 13th International Conference on Natural Language Generation - Held online Dublin City University, Dublin, Ireland
Duration: 15 Dec 202018 Dec 2020
Conference number: 13


ConferenceProceedings of the 13th International Conference on Natural Language Generation
Abbreviated titleINLG 2020
Internet address

Bibliographical note

We would like to thank our reviewers for their insightful feedback and questions.
The work presented here is partially funded by the Engineering and Physical Sciences Research Council (EPSRC), which funds Craig Thomson under a National Productivity Investment Fund Doctoral Studentship (EP/R512412/1).

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