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        <title>Classical Sequence Match Is a Competitive Few-Shot One-Class Learner</title>
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    <abstract>Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers. The models also show superiority even in the few-shot scenarios. In this paper, we revisit the classical methods and propose a new few-shot alternative. Specifically, we investigate the few-shot one-class problem, which actually takes a known sample as a reference to detect whether an unknown instance belongs to the same class. This problem can be studied from the perspective of sequence match. It is shown that with meta-learning, the classical sequence match method, i.e. Compare-Aggregate, significantly outperforms transformer ones. The classical approach requires much less training cost. Furthermore, we perform an empirical comparison between two kinds of sequence match approaches under simple fine-tuning and meta-learning. Meta-learning causes the transformer models’ features to have high-correlation dimensions. The reason is closely related to the number of layers and heads of transformer models. Experimental codes and data are available at https://github.com/hmt2014/FewOne.</abstract>
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%0 Conference Proceedings
%T Classical Sequence Match Is a Competitive Few-Shot One-Class Learner
%A Hu, Mengting
%A Gao, Hang
%A Bai, Yinhao
%A Liu, Mingming
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F hu-etal-2022-classical
%X Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers. The models also show superiority even in the few-shot scenarios. In this paper, we revisit the classical methods and propose a new few-shot alternative. Specifically, we investigate the few-shot one-class problem, which actually takes a known sample as a reference to detect whether an unknown instance belongs to the same class. This problem can be studied from the perspective of sequence match. It is shown that with meta-learning, the classical sequence match method, i.e. Compare-Aggregate, significantly outperforms transformer ones. The classical approach requires much less training cost. Furthermore, we perform an empirical comparison between two kinds of sequence match approaches under simple fine-tuning and meta-learning. Meta-learning causes the transformer models’ features to have high-correlation dimensions. The reason is closely related to the number of layers and heads of transformer models. Experimental codes and data are available at https://github.com/hmt2014/FewOne.
%U https://aclanthology.org/2022.coling-1.419
%P 4728-4740

Markdown (Informal)

[Classical Sequence Match Is a Competitive Few-Shot One-Class Learner](https://aclanthology.org/2022.coling-1.419) (Hu et al., COLING 2022)

• Classical Sequence Match Is a Competitive Few-Shot One-Class Learner (Hu et al., COLING 2022)

ACL

• Mengting Hu, Hang Gao, Yinhao Bai, and Mingming Liu. 2022. Classical Sequence Match Is a Competitive Few-Shot One-Class Learner. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4728–4740, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.