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        <title>AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation</title>
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            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
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            <namePart type="given">Saad</namePart>
            <namePart type="family">Mahamood</namePart>
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            <namePart type="given">Nguyen</namePart>
            <namePart type="given">Le</namePart>
            <namePart type="family">Minh</namePart>
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    <abstract>Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints. The code is available at https://github.com/megagonlabs/autotemplate</abstract>
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%0 Conference Proceedings
%T AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation
%A Iso, Hayate
%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 iso-2024-autotemplate-simple
%X Lexically constrained text generation is one of the constrained text generation tasks, which aims to generate text that covers all the given constraint lexicons. While the existing approaches tackle this problem using a lexically constrained beam search algorithm or dedicated model using non-autoregressive decoding, there is a trade-off between the generated text quality and the hard constraint satisfaction. We introduce AutoTemplate, a simple yet effective lexically constrained text generation framework divided into template generation and lexicalization tasks. The template generation is to generate the text with the placeholders, and lexicalization replaces them into the constraint lexicons to perform lexically constrained text generation. We conducted the experiments on two tasks: keywords-to-sentence generations and entity-guided summarization. Experimental results show that the AutoTemplate outperforms the competitive baselines on both tasks while satisfying the hard lexical constraints. The code is available at https://github.com/megagonlabs/autotemplate
%U https://aclanthology.org/2024.inlg-main.1
%P 1-12

Markdown (Informal)

[AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation](https://aclanthology.org/2024.inlg-main.1) (Iso, INLG 2024)

• AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation (Iso, INLG 2024)

ACL

• Hayate Iso. 2024. AutoTemplate: A Simple Recipe for Lexically Constrained Text Generation. In Proceedings of the 17th International Natural Language Generation Conference, pages 1–12, Tokyo, Japan. Association for Computational Linguistics.