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A case-based approach to data-to-text generation.

Upadhyay, Ashish; Massie, Stewart; Singh, Ritwik Kumar; Gupta, Garima; Ojha, Muneendra

Authors

Ritwik Kumar Singh

Garima Gupta

Muneendra Ojha



Contributors

Antonio A. Sánchez-Ruiz
Editor

Michael W. Floyd
Editor

Abstract

Traditional Data-to-Text Generation (D2T) systems utilise carefully crafted domain specific rules and templates to generate high quality accurate texts. More recent approaches use neural systems to learn domain rules from the training data to produce very fluent and diverse texts. However, there is a trade-off with rule-based systems producing accurate text but that may lack variation, while learning-based systems produce more diverse texts but often with poorer accuracy. In this paper, we propose a Case-Based approach for D2T that mitigates the impact of this trade-off by dynamically selecting templates from the training corpora. In our approach we develop a novel case-alignment based, feature weighing method that is used to build an effective similarity measure. Extensive experimentation is performed on a sports domain dataset. Through Extractive Evaluation metrics, we demonstrate the benefit of the CBR system over a rule-based baseline and a neural benchmark.

Citation

UPADHYAY, A., MASSIE, S., SINGH, R.K., GUPTA, G. and OJHA, M. 2021. A case-based approach to data-to-text generation. In Sánchez-Ruiz, A.A. and Floyd, M.W. (eds.) Case-based reasoning research and development: proceedings of 29th International conference case-based reasoning 2021 (ICCBR 2021), 13-16 September 2021, Salamanca, Spain. Lecture notes in computer science (LNCS), 12877. Cham: Springer [online], pages 232-247. Available from: https://doi.org/10.1007/978-3-030-86957-1_16

Conference Name 29th International conference on case-based reasoning 2021 (ICCBR 2021)
Conference Location Salamanca, Spain
Start Date Sep 13, 2021
End Date Sep 16, 2021
Acceptance Date Jun 11, 2021
Online Publication Date Sep 10, 2021
Publication Date Dec 31, 2021
Deposit Date Oct 26, 2021
Publicly Available Date Oct 26, 2021
Publisher Springer
Pages 232-247
Series Title Lecture notes in computer science (LNCS)
Series Number 12877
Series ISSN 0302-9743
Book Title Case-based reasoning research and development
ISBN 9783030869564
DOI https://doi.org/10.1007/978-3-030-86957-1_16
Keywords Data-to-text; Textual case-based reasoning (Textual CBR); Feature weighting
Public URL https://rgu-repository.worktribe.com/output/1482039
Related Public URLs https://rgu-repository.worktribe.com/output/1512837

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Copyright Statement
The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-86957-1_16. This accepted manuscript is subject to Springer Nature's AM terms of use.







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