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            <title>Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)</title>
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    <abstract>Research on data generation and augmentation has been focused majorly around enhancing generation models, leaving a notable gap in the exploration and refinement of methods for evaluating synthetic data. There are several text similarity metrics within the context of generated data filtering which can impact the performance of specific Natural Language Understanding (NLU) tasks, specifically focusing on intent and sentiment classification. In this study, we propose RankAug, a text-ranking approach that detects and filters out the top augmented texts in terms of being most similar in meaning with lexical and syntactical diversity. Through experiments conducted on multiple datasets, we demonstrate that the judicious selection of filtering techniques can yield a substantial improvement of up to 35% in classification accuracy for under-represented classes.</abstract>
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%0 Conference Proceedings
%T RankAug: Augmented data ranking for text classification
%A Roy, Tiasa
%A Basu, Priyam
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F roy-basu-2023-rankaug
%X Research on data generation and augmentation has been focused majorly around enhancing generation models, leaving a notable gap in the exploration and refinement of methods for evaluating synthetic data. There are several text similarity metrics within the context of generated data filtering which can impact the performance of specific Natural Language Understanding (NLU) tasks, specifically focusing on intent and sentiment classification. In this study, we propose RankAug, a text-ranking approach that detects and filters out the top augmented texts in terms of being most similar in meaning with lexical and syntactical diversity. Through experiments conducted on multiple datasets, we demonstrate that the judicious selection of filtering techniques can yield a substantial improvement of up to 35% in classification accuracy for under-represented classes.
%U https://aclanthology.org/2023.gem-1.26
%P 317-323

Markdown (Informal)

[RankAug: Augmented data ranking for text classification](https://aclanthology.org/2023.gem-1.26) (Roy & Basu, GEM-WS 2023)

• RankAug: Augmented data ranking for text classification (Roy & Basu, GEM-WS 2023)

ACL

• Tiasa Roy and Priyam Basu. 2023. RankAug: Augmented data ranking for text classification. In Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 317–323, Singapore. Association for Computational Linguistics.