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        <title>Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model‘s abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.</abstract>
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%0 Conference Proceedings
%T Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
%A Schick, Timo
%A Schmid, Helmut
%A Schütze, Hinrich
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F schick-etal-2020-automatically
%X A recent approach for few-shot text classification is to convert textual inputs to cloze questions that contain some form of task description, process them with a pretrained language model and map the predicted words to labels. Manually defining this mapping between words and labels requires both domain expertise and an understanding of the language model‘s abilities. To mitigate this issue, we devise an approach that automatically finds such a mapping given small amounts of training data. For a number of tasks, the mapping found by our approach performs almost as well as hand-crafted label-to-word mappings.
%R 10.18653/v1/2020.coling-main.488
%U https://aclanthology.org/2020.coling-main.488/
%U https://doi.org/10.18653/v1/2020.coling-main.488
%P 5569-5578

Markdown (Informal)

[Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://aclanthology.org/2020.coling-main.488/) (Schick et al., COLING 2020)

• Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (Schick et al., COLING 2020)

ACL

• Timo Schick, Helmut Schmid, and Hinrich Schütze. 2020. Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5569–5578, Barcelona, Spain (Online). International Committee on Computational Linguistics.