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        <title>Improving User Controlled Table-To-Text Generation Robustness</title>
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    <name type="personal">
        <namePart type="given">Hanxu</namePart>
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        <namePart type="given">Yunqing</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Zhongyi</namePart>
        <namePart type="family">Yu</namePart>
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    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Perez-Beltrachini</namePart>
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    <abstract>In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cell selections); however, in practice users may select unexpected, redundant, or incoherent cell combinations (noisy cell selections). In experiments, we find that models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. We propose a fine-tuning regime with additional user-simulated noisy cell selections. Models fine-tuned with the proposed regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases; and achieve comparable state-of-the-art performance on the ToTTo dataset.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.175</identifier>
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        <url>https://aclanthology.org/2023.findings-eacl.175</url>
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%0 Conference Proceedings
%T Improving User Controlled Table-To-Text Generation Robustness
%A Hu, Hanxu
%A Liu, Yunqing
%A Yu, Zhongyi
%A Perez-Beltrachini, Laura
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hu-etal-2023-improving
%X In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cell selections); however, in practice users may select unexpected, redundant, or incoherent cell combinations (noisy cell selections). In experiments, we find that models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. We propose a fine-tuning regime with additional user-simulated noisy cell selections. Models fine-tuned with the proposed regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases; and achieve comparable state-of-the-art performance on the ToTTo dataset.
%R 10.18653/v1/2023.findings-eacl.175
%U https://aclanthology.org/2023.findings-eacl.175
%U https://doi.org/10.18653/v1/2023.findings-eacl.175
%P 2317-2324

Markdown (Informal)

[Improving User Controlled Table-To-Text Generation Robustness](https://aclanthology.org/2023.findings-eacl.175) (Hu et al., Findings 2023)

• Improving User Controlled Table-To-Text Generation Robustness (Hu et al., Findings 2023)

ACL

• Hanxu Hu, Yunqing Liu, Zhongyi Yu, and Laura Perez-Beltrachini. 2023. Improving User Controlled Table-To-Text Generation Robustness. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2317–2324, Dubrovnik, Croatia. Association for Computational Linguistics.