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        <title>Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages</title>
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        <namePart type="given">Piyawat</namePart>
        <namePart type="family">Lertvittayakumjorn</namePart>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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            <namePart type="given">Xiyan</namePart>
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    <abstract>Prompt-based learning has shown its effectiveness in few-shot text classification. A key factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection may not yield the optimal words for a given language model, potentially leading to subpar classification performance, especially in mid-to-low resource languages with weaker language models. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting manual labels for improved few-shot classification results. Specifically, we utilize the label name along with the conjunction “and” to induce the model to generate more effective words for the verbalizer. Experimental results on four mid-to-low resource Southeast Asian languages demonstrate that LAAV significantly outperforms existing verbalizers.</abstract>
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%0 Conference Proceedings
%T Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages
%A Thaminkaew, Thanakorn
%A Lertvittayakumjorn, Piyawat
%A Vateekul, Peerapon
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F thaminkaew-etal-2024-label
%X Prompt-based learning has shown its effectiveness in few-shot text classification. A key factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the simplest and widely acknowledged verbalizer employs manual labels to represent the classes. However, manual selection may not yield the optimal words for a given language model, potentially leading to subpar classification performance, especially in mid-to-low resource languages with weaker language models. Therefore, we propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting manual labels for improved few-shot classification results. Specifically, we utilize the label name along with the conjunction “and” to induce the model to generate more effective words for the verbalizer. Experimental results on four mid-to-low resource Southeast Asian languages demonstrate that LAAV significantly outperforms existing verbalizers.
%U https://aclanthology.org/2024.acl-srw.19
%P 195-203

Markdown (Informal)

[Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages](https://aclanthology.org/2024.acl-srw.19) (Thaminkaew et al., ACL 2024)

• Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages (Thaminkaew et al., ACL 2024)

ACL

• Thanakorn Thaminkaew, Piyawat Lertvittayakumjorn, and Peerapon Vateekul. 2024. Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 195–203, Bangkok, Thailand. Association for Computational Linguistics.