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        <title>ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective</title>
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        <namePart type="given">Sultan</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pre-training cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.108</identifier>
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%0 Conference Proceedings
%T ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective
%A Alrowili, Sultan
%A Shanker, Vijay
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F alrowili-shanker-2021-arabictransformer-efficient
%X Pre-training Transformer-based models such as BERT and ELECTRA on a collection of Arabic corpora, demonstrated by both AraBERT and AraELECTRA, shows an impressive result on downstream tasks. However, pre-training Transformer-based language models is computationally expensive, especially for large-scale models. Recently, Funnel Transformer has addressed the sequential redundancy inside Transformer architecture by compressing the sequence of hidden states, leading to a significant reduction in the pre-training cost. This paper empirically studies the performance and efficiency of building an Arabic language model with Funnel Transformer and ELECTRA objective. We find that our model achieves state-of-the-art results on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.
%R 10.18653/v1/2021.findings-emnlp.108
%U https://aclanthology.org/2021.findings-emnlp.108
%U https://doi.org/10.18653/v1/2021.findings-emnlp.108
%P 1255-1261

Markdown (Informal)

[ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective](https://aclanthology.org/2021.findings-emnlp.108) (Alrowili & Shanker, Findings 2021)

• ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (Alrowili & Shanker, Findings 2021)

ACL

• Sultan Alrowili and Vijay Shanker. 2021. ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1255–1261, Punta Cana, Dominican Republic. Association for Computational Linguistics.