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        <title>Developing Prefix-Tuning Models for Hierarchical Text Classification</title>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <abstract>Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text. For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories. In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too—the conventional fine-tuning process needs to modify and save models with a huge number of parameters. This is becoming more critical for HTC in both global and local modelling—the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy. The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances. Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters. Exploring prefix turning for HTC is hence highly desirable and has timely impact. In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC. Our experiment shows that the prefix-tuning model only needs less than 1% of parameters and can achieve performance comparable to regular full fine-tuning. We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-industry.39</identifier>
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%0 Conference Proceedings
%T Developing Prefix-Tuning Models for Hierarchical Text Classification
%A Chen, Lei
%A Chou, Houwei
%A Zhu, Xiaodan
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-etal-2022-developing
%X Hierarchical text classification (HTC) is a key problem and task in many industrial applications, which aims to predict labels organized in a hierarchy for given input text. For example, HTC can group the descriptions of online products into a taxonomy or organizing customer reviews into a hierarchy of categories. In real-life applications, while Pre-trained Language Models (PLMs) have dominated many NLP tasks, they face significant challenges too—the conventional fine-tuning process needs to modify and save models with a huge number of parameters. This is becoming more critical for HTC in both global and local modelling—the latter needs to learn multiple classifiers at different levels/nodes in a hierarchy. The concern will be even more serious since PLM sizes are continuing to increase in order to attain more competitive performances. Most recently, prefix tuning has become a very attractive technology by only tuning and saving a tiny set of parameters. Exploring prefix turning for HTC is hence highly desirable and has timely impact. In this paper, we investigate prefix tuning on HTC in two typical setups: local and global HTC. Our experiment shows that the prefix-tuning model only needs less than 1% of parameters and can achieve performance comparable to regular full fine-tuning. We demonstrate that using contrastive learning in learning prefix vectors can further improve HTC performance.
%R 10.18653/v1/2022.emnlp-industry.39
%U https://aclanthology.org/2022.emnlp-industry.39
%U https://doi.org/10.18653/v1/2022.emnlp-industry.39
%P 390-397

Markdown (Informal)

[Developing Prefix-Tuning Models for Hierarchical Text Classification](https://aclanthology.org/2022.emnlp-industry.39) (Chen et al., EMNLP 2022)

• Developing Prefix-Tuning Models for Hierarchical Text Classification (Chen et al., EMNLP 2022)

ACL

• Lei Chen, Houwei Chou, and Xiaodan Zhu. 2022. Developing Prefix-Tuning Models for Hierarchical Text Classification. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 390–397, Abu Dhabi, UAE. Association for Computational Linguistics.