EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang
Abstract
Product attribute value extraction involves identifying the specific values associated with various attributes from a product profile. While existing methods often prioritize the development of effective models to improve extraction performance, there has been limited emphasis on extraction efficiency. However, in real-world scenarios, products are typically associated with multiple attributes, necessitating multiple extractions to obtain all corresponding values. In this work, we propose an Efficient product Attribute Value Extraction (EAVE) approach via lightweight sparse-layer interaction. Specifically, we employ a heavy encoder to separately encode the product context and attribute. The resulting non-interacting heavy representations of the context can be cached and reused for all attributes. Additionally, we introduce a light encoder to jointly encode the context and the attribute, facilitating lightweight interactions between them. To enrich the interaction within the lightweight encoder, we design a sparse-layer interaction module to fuse the non-interacting heavy representation into the lightweight encoder. Comprehensive evaluation on two benchmarks demonstrate that our method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large. Our code is available at: https://anonymous.4open.science/r/EAVE-EA18.
BibTeX
@inproceedings{yang-etal-2024-eave,
title = "{EAVE}: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction",
author = "Yang, Li and
Wang, Qifan and
Chi, Jianfeng and
Liu, Jiahao and
Wang, Jingang and
Feng, Fuli and
Xu, Zenglin and
Fang, Yi and
Huang, Lifu and
Liu, Dongfang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.80/",
doi = "10.18653/v1/2024.findings-emnlp.80",
pages = "1491--1505"
}