@inproceedings{guo-etal-2023-comave,
title = "{C}o{M}ave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction",
author = "Guo, Xinnan and
Deng, Wentao and
Chen, Yongrui and
Li, Yang and
Zhou, Mengdi and
Qi, Guilin and
Wu, Tianxing and
Yang, Dong and
Wang, Liubin and
Pan, Yong",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.373",
doi = "10.18653/v1/2023.findings-acl.373",
pages = "6007--6018",
abstract = "Attribute Value Extraction (AVE) aims to automatically obtain attribute value pairs from product descriptions to aid e-commerce. Despite the progressive performance of existing approaches in e-commerce platforms, they still suffer from two challenges: 1) difficulty in identifying values at different scales simultaneously; 2) easy confusion by some highly similar fine-grained attributes. This paper proposes a pre-training technique for AVE to address these issues. In particular, we first improve the conventional token-level masking strategy, guiding the language model to understand multi-scale values by recovering spans at the phrase and sentence level. Second, we apply clustering to build a challenging negative set for each example and design a pre-training objective based on contrastive learning to force the model to discriminate similar attributes. Comprehensive experiments show that our solution provides a significant improvement over traditional pre-trained models in the AVE task, and achieves state-of-the-art on four benchmarks.",
}
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<abstract>Attribute Value Extraction (AVE) aims to automatically obtain attribute value pairs from product descriptions to aid e-commerce. Despite the progressive performance of existing approaches in e-commerce platforms, they still suffer from two challenges: 1) difficulty in identifying values at different scales simultaneously; 2) easy confusion by some highly similar fine-grained attributes. This paper proposes a pre-training technique for AVE to address these issues. In particular, we first improve the conventional token-level masking strategy, guiding the language model to understand multi-scale values by recovering spans at the phrase and sentence level. Second, we apply clustering to build a challenging negative set for each example and design a pre-training objective based on contrastive learning to force the model to discriminate similar attributes. Comprehensive experiments show that our solution provides a significant improvement over traditional pre-trained models in the AVE task, and achieves state-of-the-art on four benchmarks.</abstract>
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%0 Conference Proceedings
%T CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction
%A Guo, Xinnan
%A Deng, Wentao
%A Chen, Yongrui
%A Li, Yang
%A Zhou, Mengdi
%A Qi, Guilin
%A Wu, Tianxing
%A Yang, Dong
%A Wang, Liubin
%A Pan, Yong
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F guo-etal-2023-comave
%X Attribute Value Extraction (AVE) aims to automatically obtain attribute value pairs from product descriptions to aid e-commerce. Despite the progressive performance of existing approaches in e-commerce platforms, they still suffer from two challenges: 1) difficulty in identifying values at different scales simultaneously; 2) easy confusion by some highly similar fine-grained attributes. This paper proposes a pre-training technique for AVE to address these issues. In particular, we first improve the conventional token-level masking strategy, guiding the language model to understand multi-scale values by recovering spans at the phrase and sentence level. Second, we apply clustering to build a challenging negative set for each example and design a pre-training objective based on contrastive learning to force the model to discriminate similar attributes. Comprehensive experiments show that our solution provides a significant improvement over traditional pre-trained models in the AVE task, and achieves state-of-the-art on four benchmarks.
%R 10.18653/v1/2023.findings-acl.373
%U https://aclanthology.org/2023.findings-acl.373
%U https://doi.org/10.18653/v1/2023.findings-acl.373
%P 6007-6018
Markdown (Informal)
[CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction](https://aclanthology.org/2023.findings-acl.373) (Guo et al., Findings 2023)
ACL
- Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, and Yong Pan. 2023. CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6007–6018, Toronto, Canada. Association for Computational Linguistics.