Papers with AVE
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. |
Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023.emnlp-industry)
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| Challenge: | scalability of attribute-value extraction (AVE) task is key for a large number of products . a question-answering (QA)-based approach is better for AVE, but requires a larger number of classes to be scalable. |
| Approach: | They propose a question-answering-based approach that additionally inputs the target attribute as a query to extract its values. |
| Outcome: | The proposed approach outperforms a classical approach on real-word e-commerce datasets in accuracy and speed. |
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction (2024.findings-acl)
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Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea
| Challenge: | Existing datasets for attribute value extraction focus on explicit attribute values while neglecting the implicit ones. |
| Approach: | They present a multimodal dataset for implicit attribute value extraction that includes AVE and multimodality. |
| Outcome: | The proposed dataset includes 68k training and 1.6k testing data across five domains. |
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. |
MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction (2026.findings-eacl)
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| Challenge: | Implicit Attribute Value Extraction (AVE) is essential for accurately representing products in e-commerce due to the complexity of multidimensional data and gaps in vision-text understanding. |
| Approach: | They propose a multi-agent framework that employs multiple MLLM agents to iteratively refine inferences through debate rounds. |
| Outcome: | The proposed framework improves inference performance and robustness through debate rounds. |
CoMave: Contrastive Pre-training with Multi-scale Masking for Attribute Value Extraction (2023.findings-acl)
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Xinnan Guo, Wentao Deng, Yongrui Chen, Yang Li, Mengdi Zhou, Guilin Qi, Tianxing Wu, Dong Yang, Liubin Wang, Yong Pan
| 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. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| 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. |
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)
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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. |
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction (2026.findings-acl)
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| Challenge: | Existing methods for text generation require multiple independent sequences to be decoded in parallel. |
| Approach: | They propose an algorithm that accelerates offline decoding by leveraging shared memory and computation across batches. |
| Outcome: | Experiments show that attribute-value pairs are conditionally independent, enabling decoding in parallel up to 96 tokens per prompt. |