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        <title>DREAM: Deployment of Recombination and Ensembles in Argument Mining</title>
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    <abstract>Current approaches to Argument Mining (AM) tend to take a holistic or black-box view of the overall pipeline. This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions. To that end, it presents the Deployment of Recombination and Ensemble methods for Argument Miners (DREAM) framework that allows for the (automated) combination of AM components. Using ensemble methods, DREAM combines sets of AM systems to improve accuracy for the four tasks in the AM pipeline. Furthermore, it leverages recombination by using different argument miners elements throughout the pipeline. Experiments with five systems previously included in a benchmark show that the systems combined with DREAM can outperform the previous best single systems in terms of accuracy measured by an AM benchmark.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.320</identifier>
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%0 Conference Proceedings
%T DREAM: Deployment of Recombination and Ensembles in Argument Mining
%A Ruosch, Florian
%A Sarasua, Cristina
%A Bernstein, Abraham
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ruosch-etal-2023-dream
%X Current approaches to Argument Mining (AM) tend to take a holistic or black-box view of the overall pipeline. This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions. To that end, it presents the Deployment of Recombination and Ensemble methods for Argument Miners (DREAM) framework that allows for the (automated) combination of AM components. Using ensemble methods, DREAM combines sets of AM systems to improve accuracy for the four tasks in the AM pipeline. Furthermore, it leverages recombination by using different argument miners elements throughout the pipeline. Experiments with five systems previously included in a benchmark show that the systems combined with DREAM can outperform the previous best single systems in terms of accuracy measured by an AM benchmark.
%R 10.18653/v1/2023.emnlp-main.320
%U https://aclanthology.org/2023.emnlp-main.320
%U https://doi.org/10.18653/v1/2023.emnlp-main.320
%P 5277-5290

Markdown (Informal)

[DREAM: Deployment of Recombination and Ensembles in Argument Mining](https://aclanthology.org/2023.emnlp-main.320) (Ruosch et al., EMNLP 2023)

• DREAM: Deployment of Recombination and Ensembles in Argument Mining (Ruosch et al., EMNLP 2023)

ACL

• Florian Ruosch, Cristina Sarasua, and Abraham Bernstein. 2023. DREAM: Deployment of Recombination and Ensembles in Argument Mining. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5277–5290, Singapore. Association for Computational Linguistics.