Challenge: Existing methods for AVE are limited on rare attributes due to poor generalization ability.
Approach: They propose to leverage pretraining and transfer learning to address weaknesses in existing methods.
Outcome: The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources.

Similar Papers

Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction (2022.acl-short)

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Challenge: Existing approaches to extract value from product data for a large number of attributes are not effective for rare and ambiguous attributes.
Approach: They propose to use attributes as knowledge to expand AVE queries by retrieving possible answers from training data.
Outcome: The proposed model improves on a cleaned version of AliExpress dataset for rare and ambiguous attributes, especially for rare attributes.
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)

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Challenge: Existing methods to extract product features from unstructured text still suffer from problems . e-commerce platforms are focusing on multi-scale values, which can be confusing .
Approach: They propose a pre-training technique to automatically obtain attribute value pairs from product descriptions to aid e-commerce.
Outcome: The proposed method improves on the existing token-level masking strategy and achieves state-of-the-art on four benchmarks.
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)

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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.
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning (2020.emnlp-main)

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Challenge: Existing methods for open attribute value extraction for emerging entities are noisy or incomplete, even missing.
Approach: They propose a knowledge-guided reinforcement learning framework for open attribute value extraction for emerging entities.
Outcome: The proposed framework outperforms baselines by 16.5 - 27.8%.
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding (2021.acl-long)

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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.
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction (2022.emnlp-industry)

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Challenge: Existing reading comprehension models can over-generate attribute values which hinders precision.
Approach: They propose a product attribute value extraction task that captures key factual information from product descriptions and a new end-to-end pipeline framework called Ask-and-Verify.
Outcome: The proposed framework outperforms existing models by up to 3.1% F1 absolute improvement points while scaling to thousands of attributes.
Stronger, Lighter, Better: Towards Life-Long Attribute Value Extraction for E-Commerce Products (2024.findings-acl)

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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.
Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

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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.
Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes (2023.acl-industry)

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Challenge: E-commerce websites often don’t label or mislabel attributes of products .
Approach: They propose a multi-modal product attribute generation system that extracts product attributes from the product pages of eCommerce stores by using both text and images.
Outcome: The proposed model improves the recall@90P accuracy by 10.16% and 6.9 from the state-of-the-art models.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis (2020.acl-main)

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Challenge: sentiment knowledge is ignored in sentiment analysis, despite its use in pretraining.
Approach: They propose to use sentiment knowledge to learn a unified sentiment representation for multiple sentiment analysis tasks.
Outcome: The proposed method outperforms strong pre-training baseline on three kinds of sentiment tasks.

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