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        <title>Better Few-Shot Relation Extraction with Label Prompt Dropout</title>
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    <abstract>Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way to leverage such label information in the learning process is an important research question. Existing works largely assume such textual labels are always present during both learning and prediction. In this work, we argue that such approaches may not always lead to optimal results. Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.471</identifier>
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%0 Conference Proceedings
%T Better Few-Shot Relation Extraction with Label Prompt Dropout
%A Zhang, Peiyuan
%A Lu, Wei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-lu-2022-better
%X Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way to leverage such label information in the learning process is an important research question. Existing works largely assume such textual labels are always present during both learning and prediction. In this work, we argue that such approaches may not always lead to optimal results. Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.
%R 10.18653/v1/2022.emnlp-main.471
%U https://aclanthology.org/2022.emnlp-main.471
%U https://doi.org/10.18653/v1/2022.emnlp-main.471
%P 6996-7006

Markdown (Informal)

[Better Few-Shot Relation Extraction with Label Prompt Dropout](https://aclanthology.org/2022.emnlp-main.471) (Zhang & Lu, EMNLP 2022)

• Better Few-Shot Relation Extraction with Label Prompt Dropout (Zhang & Lu, EMNLP 2022)

ACL

• Peiyuan Zhang and Wei Lu. 2022. Better Few-Shot Relation Extraction with Label Prompt Dropout. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6996–7006, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.