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- W4288087029 abstract "While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data by iteratively selecting a small number of examples for manual annotation based on their estimated utility in training the given model. In this paper, we argue that since AL strategies choose examples independently, they may potentially select similar examples, all of which may not contribute significantly to the learning process. Our proposed approach, Active$mathbf{^2}$ Learning (A$mathbf{^2}$L), actively adapts to the deep learning model being trained to eliminate such redundant examples chosen by an AL strategy. We show that A$mathbf{^2}$L is widely applicable by using it in conjunction with several different AL strategies and NLP tasks. We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by $approx mathbf{3-25%}$ on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead." @default.
- W4288087029 created "2022-07-28" @default.
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- W4288087029 date "2019-11-01" @default.
- W4288087029 modified "2023-09-27" @default.
- W4288087029 title "Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation" @default.
- W4288087029 doi "https://doi.org/10.48550/arxiv.1911.00234" @default.
- W4288087029 hasPublicationYear "2019" @default.
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