EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)
Copied to clipboard
Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu
| Challenge: | Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values. |
| Approach: | They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction. |
| Outcome: | The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large. |
Similar Papers
Generative Models for Product Attribute Extraction (2023.emnlp-industry)
Copied to clipboard
| Challenge: | generative models are used for product attribute extraction, a new field in information extraction and e-commerce. |
| Approach: | They analyze generative models for product attribute extraction and demonstrate their utility . they perform experiments on Amazon and MAVE product attribute datasets . |
| Outcome: | The proposed model can generate implicit attribute values, which state-of-the-art models are unable to extract. |
TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)
Copied to clipboard
Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate . |
| Approach: | They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity . |
| Outcome: | The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds. |
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title (P19-1)
Copied to clipboard
| Challenge: | Existing models treat each attribute as an entity type and build one set of NER tags for each of them, leading to scalability issues. |
| Approach: | They propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates promising results for 8,906 attributes. |
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product (2020.emnlp-main)
Copied to clipboard
| Challenge: | In the real world, product attribute values are incomplete and vary over time, which hinders practical applications. |
| Approach: | They propose a multimodal method to jointly predict product attributes and extract values from product images using multimodal product information. |
| Outcome: | The proposed method can predict product attributes and extract values from product images with the help of product images. |
Stronger, Lighter, Better: Towards Life-Long Attribute Value Extraction for E-Commerce Products (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for attribute value extraction struggle for parameter efficiency and reliability due to data contamination and catastrophic forgetting. |
| Approach: | They propose to decouple product type and attribute to promote de-contamination and parameter efficiency while scaling up. |
| Outcome: | The proposed model achieves state-of-the-art performance with affordable parameter size, least historical knowledge forgetting, and greatest robustness against noises. |
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach (2023.acl-long)
Copied to clipboard
| Challenge: | Existing task setting for attribute mining on e-commerce products is closed-world, but recent work has moved towards open-world aspect. |
| Approach: | They propose a task setting for attribute mining on e-commerce products that uses a high-quality seed attribute set bootstrapped from existing resources. |
| Outcome: | The proposed approach surpasses baselines on existing attributes by 12 F1 and discovers values from 39% new attributes. |
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)
Copied to clipboard
Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen
| Challenge: | Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability. |
| Approach: | They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain. |
| Outcome: | The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset. |
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)
Copied to clipboard
Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, Hongzhi Zhang
| Challenge: | Existing approaches to extract attribute values from product descriptions are incomplete and noisy due to the tedious nature of this task. |
| Approach: | They propose a framework to extract attributes from product descriptions to acquire implicit attributes in addition to the explicit ones. |
| Outcome: | The proposed framework outperforms existing methods on the extraction of implicit attribute values while achieving comparable performance for the explicit ones. |
AttriSage: Product Attribute Value Extraction Using Graph Neural Networks (2024.eacl-srw)
Copied to clipboard
| Challenge: | Existing methods for extracting attribute value from product descriptions are limited in their accuracy. |
| Approach: | They propose a method for extracting product attribute value from product description using graphs and neural networks. |
| Outcome: | The proposed method improves product description attribute value extraction accuracy compared to baseline methods. |
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to extract product attribute values are limited by knowledge sharing across different attributes. |
| Approach: | They propose to use adaptive decoding to handle extraction of product attribute values by parameterizing the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts module. |
| Outcome: | The proposed model is able to handle multiple attributes without sharing the entire network parameters across all attributes. |