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        <title>Efficient Nearest Neighbor Emotion Classification with BERT-whitening</title>
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    <abstract>Retrieval-based methods have been proven effective in many NLP tasks. Previous methods use representations from the pre-trained model for similarity search directly. However, the sentence representations from the pre-trained model like BERT perform poorly in retrieving semantically similar sentences, resulting in poor performance of the retrieval-based methods. In this paper, we propose kNN-EC, a simple and efficient non-parametric emotion classification (EC) method using nearest neighbor retrieval. We use BERT-whitening to get better sentence semantics, ensuring that nearest neighbor retrieval works. Meanwhile, BERT-whitening can also reduce memory storage of datastore and accelerate retrieval speed, solving the efficiency problem of the previous methods. kNN-EC average improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.312</identifier>
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%0 Conference Proceedings
%T Efficient Nearest Neighbor Emotion Classification with BERT-whitening
%A Yin, Wenbiao
%A Shang, Lin
%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 yin-shang-2022-efficient
%X Retrieval-based methods have been proven effective in many NLP tasks. Previous methods use representations from the pre-trained model for similarity search directly. However, the sentence representations from the pre-trained model like BERT perform poorly in retrieving semantically similar sentences, resulting in poor performance of the retrieval-based methods. In this paper, we propose kNN-EC, a simple and efficient non-parametric emotion classification (EC) method using nearest neighbor retrieval. We use BERT-whitening to get better sentence semantics, ensuring that nearest neighbor retrieval works. Meanwhile, BERT-whitening can also reduce memory storage of datastore and accelerate retrieval speed, solving the efficiency problem of the previous methods. kNN-EC average improves the pre-trained model by 1.17 F1-macro on two emotion classification datasets.
%R 10.18653/v1/2022.emnlp-main.312
%U https://aclanthology.org/2022.emnlp-main.312
%U https://doi.org/10.18653/v1/2022.emnlp-main.312
%P 4738-4745

Markdown (Informal)

[Efficient Nearest Neighbor Emotion Classification with BERT-whitening](https://aclanthology.org/2022.emnlp-main.312) (Yin & Shang, EMNLP 2022)

• Efficient Nearest Neighbor Emotion Classification with BERT-whitening (Yin & Shang, EMNLP 2022)

ACL

• Wenbiao Yin and Lin Shang. 2022. Efficient Nearest Neighbor Emotion Classification with BERT-whitening. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4738–4745, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.