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        <title>Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model</title>
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        <namePart type="given">Chris</namePart>
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    <name type="personal">
        <namePart type="given">Travis</namePart>
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    <abstract>This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.</abstract>
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%0 Journal Article
%T Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model
%A van der Lee, Chris
%A Castro Ferreira, Thiago
%A Emmery, Chris
%A Wiltshire, Travis J.
%A Krahmer, Emiel
%J Computational Linguistics
%D 2023
%8 September
%I MIT Press
%C Cambridge, MA
%F van-der-lee-etal-2023-neural
%X This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.
%R 10.1162/coli_a_00484
%U https://aclanthology.org/2023.cl-3.2
%U https://doi.org/10.1162/coli_a_00484
%P 555-611

Markdown (Informal)

[Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model](https://aclanthology.org/2023.cl-3.2) (van der Lee et al., CL 2023)

• Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model (van der Lee et al., CL 2023)

ACL

• Chris van der Lee, Thiago Castro Ferreira, Chris Emmery, Travis J. Wiltshire, and Emiel Krahmer. 2023. Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model. Computational Linguistics:555–611.