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        <title>Does local pruning offer task-specific models to learn effectively ?</title>
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        <namePart type="given">Abhishek</namePart>
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    <abstract>The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models. However, less attention has been given to compress the task-specific models. In this work, we investigate the different methods of unstructured pruning on task-specific models for Aspect-based Sentiment Analysis (ABSA) tasks. Specifically, we analyze differences in the learning dynamics of pruned models by using the standard pruning techniques to achieve high-performing sparse networks. We develop a hypothesis to demonstrate the effectiveness of local pruning over global pruning considering a simple CNN model. Later, we utilize the hypothesis to demonstrate the efficacy of the pruned state-of-the-art model compared to the over-parameterized state-of-the-art model under two settings, the first considering the baselines for the same task used for generating the hypothesis, i.e., aspect extraction and the second considering a different task, i.e., sentiment analysis. We also provide discussion related to the generalization of the pruning hypothesis.</abstract>
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%0 Conference Proceedings
%T Does local pruning offer task-specific models to learn effectively ?
%A Mishra, Abhishek Kumar
%A Chakraborty, Mohna
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F mishra-chakraborty-2021-local-pruning
%X The need to deploy large-scale pre-trained models on edge devices under limited computational resources has led to substantial research to compress these large models. However, less attention has been given to compress the task-specific models. In this work, we investigate the different methods of unstructured pruning on task-specific models for Aspect-based Sentiment Analysis (ABSA) tasks. Specifically, we analyze differences in the learning dynamics of pruned models by using the standard pruning techniques to achieve high-performing sparse networks. We develop a hypothesis to demonstrate the effectiveness of local pruning over global pruning considering a simple CNN model. Later, we utilize the hypothesis to demonstrate the efficacy of the pruned state-of-the-art model compared to the over-parameterized state-of-the-art model under two settings, the first considering the baselines for the same task used for generating the hypothesis, i.e., aspect extraction and the second considering a different task, i.e., sentiment analysis. We also provide discussion related to the generalization of the pruning hypothesis.
%U https://aclanthology.org/2021.ranlp-srw.17
%P 118-125

Markdown (Informal)

[Does local pruning offer task-specific models to learn effectively ?](https://aclanthology.org/2021.ranlp-srw.17) (Mishra & Chakraborty, RANLP 2021)

• Does local pruning offer task-specific models to learn effectively ? (Mishra & Chakraborty, RANLP 2021)

ACL

• Abhishek Kumar Mishra and Mohna Chakraborty. 2021. Does local pruning offer task-specific models to learn effectively ?. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 118–125, Online. INCOMA Ltd..