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        <title>Reproducibility of Exploring Neural Text Simplification Models: A Review</title>
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    <abstract>The reproducibility of NLP research has drawn increased attention over the last few years. Several tools, guidelines, and metrics have been introduced to address concerns in regard to this problem; however, much work still remains to ensure widespread adoption of effective reproducibility standards. In this work, we review the reproducibility of Exploring Neural Text Simplification Models by Nisioi et al. (2017), evaluating it from three main aspects: data, software artifacts, and automatic evaluations. We discuss the challenges and issues we faced during this process. Furthermore, we explore the adequacy of current reproducibility standards. Our code, trained models, and a docker container of the environment used for training and evaluation are made publicly available.</abstract>
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%0 Conference Proceedings
%T Reproducibility of Exploring Neural Text Simplification Models: A Review
%A Arvan, Mohammad
%A Pina, Luís
%A Parde, Natalie
%Y Shaikh, Samira
%Y Ferreira, Thiago
%Y Stent, Amanda
%S Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges
%D 2022
%8 July
%I Association for Computational Linguistics
%C Waterville, Maine, USA and virtual meeting
%F arvan-etal-2022-reproducibility
%X The reproducibility of NLP research has drawn increased attention over the last few years. Several tools, guidelines, and metrics have been introduced to address concerns in regard to this problem; however, much work still remains to ensure widespread adoption of effective reproducibility standards. In this work, we review the reproducibility of Exploring Neural Text Simplification Models by Nisioi et al. (2017), evaluating it from three main aspects: data, software artifacts, and automatic evaluations. We discuss the challenges and issues we faced during this process. Furthermore, we explore the adequacy of current reproducibility standards. Our code, trained models, and a docker container of the environment used for training and evaluation are made publicly available.
%U https://aclanthology.org/2022.inlg-genchal.10
%P 62-70

Markdown (Informal)

[Reproducibility of Exploring Neural Text Simplification Models: A Review](https://aclanthology.org/2022.inlg-genchal.10) (Arvan et al., INLG 2022)

• Reproducibility of Exploring Neural Text Simplification Models: A Review (Arvan et al., INLG 2022)

ACL

• Mohammad Arvan, Luís Pina, and Natalie Parde. 2022. Reproducibility of Exploring Neural Text Simplification Models: A Review. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges, pages 62–70, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.