@inproceedings{howcroft-etal-2024-exploring-impact,
title = "Exploring the impact of data representation on neural data-to-text generation",
author = "Howcroft, David M. and
Watson, Lewis N. and
Nedopas, Olesia and
Gkatzia, Dimitra",
editor = "Mahamood, Saad and
Minh, Nguyen Le and
Ippolito, Daphne",
booktitle = "Proceedings of the 17th International Natural Language Generation Conference",
month = sep,
year = "2024",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.inlg-main.20",
pages = "243--253",
abstract = "A relatively under-explored area in research on neural natural language generation is the impact of the data representation on text quality. Here we report experiments on two leading input representations for data-to-text generation: attribute-value pairs and Resource Description Framework (RDF) triples. Evaluating the performance of encoder-decoder seq2seq models as well as recent large language models (LLMs) with both automated metrics and human evaluation, we find that the input representation does not seem to have a large impact on the performance of either purpose-built seq2seq models or LLMs. Finally, we present an error analysis of the texts generated by the LLMs and provide some insights into where these models fail.",
}
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<abstract>A relatively under-explored area in research on neural natural language generation is the impact of the data representation on text quality. Here we report experiments on two leading input representations for data-to-text generation: attribute-value pairs and Resource Description Framework (RDF) triples. Evaluating the performance of encoder-decoder seq2seq models as well as recent large language models (LLMs) with both automated metrics and human evaluation, we find that the input representation does not seem to have a large impact on the performance of either purpose-built seq2seq models or LLMs. Finally, we present an error analysis of the texts generated by the LLMs and provide some insights into where these models fail.</abstract>
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%0 Conference Proceedings
%T Exploring the impact of data representation on neural data-to-text generation
%A Howcroft, David M.
%A Watson, Lewis N.
%A Nedopas, Olesia
%A Gkatzia, Dimitra
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F howcroft-etal-2024-exploring-impact
%X A relatively under-explored area in research on neural natural language generation is the impact of the data representation on text quality. Here we report experiments on two leading input representations for data-to-text generation: attribute-value pairs and Resource Description Framework (RDF) triples. Evaluating the performance of encoder-decoder seq2seq models as well as recent large language models (LLMs) with both automated metrics and human evaluation, we find that the input representation does not seem to have a large impact on the performance of either purpose-built seq2seq models or LLMs. Finally, we present an error analysis of the texts generated by the LLMs and provide some insights into where these models fail.
%U https://aclanthology.org/2024.inlg-main.20
%P 243-253
Markdown (Informal)
[Exploring the impact of data representation on neural data-to-text generation](https://aclanthology.org/2024.inlg-main.20) (Howcroft et al., INLG 2024)
ACL