# AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding arXiv: https://arxiv.org/abs/2106.02318 ยท HTML: https://mlsanity.com/paper/2106.02318/ ## Key facts - Cited by 32 papers in the map (data through 14 Oct 2026). - Placed in MoE for recommender systems, one of 12 directions in Mixture-of-experts. - Builds on 17 of its 32 references that are in the map. - First posted on arXiv 4 Jun 2021. ## Placed in - Mixture-of-experts / [MoE for recommender systems](https://mlsanity.com/moe/moe-for-recommender-systems/) ## Builds on (most cited first, 17 of 17) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016) - [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015) - [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018) - [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018) - [Contextual Parameter Generation for Universal Neural Machine Translation](https://mlsanity.com/paper/1808.08493/) (2018) - [Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product](https://mlsanity.com/paper/2009.07162/) (2020) - [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017) - [Metadata Embeddings for User and Item Cold-start Recommendations](https://mlsanity.com/paper/1507.08439/) (2015) - [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020) - [Text Generation with Exemplar-based Adaptive Decoding](https://mlsanity.com/paper/1904.04428/) (2019) - [LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues](https://mlsanity.com/paper/1605.01652/) (2016) - [Adaptive Parameterization for Neural Dialogue Generation](https://mlsanity.com/paper/2001.06626/) (2020) - [Learning Sparse Mixture of Experts for Visual Question Answering](https://mlsanity.com/paper/1909.09192/) (2019) ## Built on by (most cited first, 30 of 32) - [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021) - [LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2403.00863/) (2024) - [WebFormer: The Web-page Transformer for Structure Information Extraction](https://mlsanity.com/paper/2202.00217/) (2022) - [OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision](https://mlsanity.com/paper/2204.13874/) (2022) - [ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction](https://mlsanity.com/paper/2310.12537/) (2023) - [Using LLMs for the Extraction and Normalization of Product Attribute Values](https://mlsanity.com/paper/2403.02130/) (2024) - [ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2404.15592/) (2024) - [Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph](https://mlsanity.com/paper/2209.15214/) (2022) - [Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction](https://mlsanity.com/paper/2206.14264/) (2022) - [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024) - [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023) - [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023) - [An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification](https://mlsanity.com/paper/2407.01137/) (2024) - [PUMGPT: A Large Vision-Language Model for Product Understanding](https://mlsanity.com/paper/2308.09568/) (2023) - [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025) - [EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction](https://mlsanity.com/paper/2406.06839/) (2024) - [Product Information Extraction using ChatGPT](https://mlsanity.com/paper/2306.14921/) (2023) - [Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction](https://mlsanity.com/paper/2308.08413/) (2023) - [SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs](https://mlsanity.com/paper/2309.05920/) (2023) - [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024) - [Exploring Generative Models for Joint Attribute Value Extraction from Product Titles](https://mlsanity.com/paper/2208.07130/) (2022) - [PV2TEA: Patching Visual Modality to Textual-Established Information Extraction](https://mlsanity.com/paper/2306.01016/) (2023) - [AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction](https://mlsanity.com/paper/2310.07137/) (2023) - [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024) - [Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes](https://mlsanity.com/paper/2409.13628/) (2024) - [MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2510.05611/) (2025) - [Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization](https://mlsanity.com/paper/2207.07278/) (2022) - [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023) - [JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction](https://mlsanity.com/paper/2311.04196/) (2023) - [VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models](https://mlsanity.com/paper/2508.11801/) (2025) ## Abstract (arXiv) Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model's capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.