EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction

• URL: http://arxiv.org/abs/2406.06839v1
• Date: Mon, 10 Jun 2024 23:06:38 GMT
• Title: EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
• Authors: Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu,
• Abstract summary: We propose an Efficient product Attribute Value Extraction (EAVE) approach via lightweight sparse-layer interaction. 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. Our method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.
• Score: 94.22610101608332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \href{https://anonymous.4open.science/r/EAVE-EA18}{here}.

Related papers

• EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM [52.016009472409166]
  EIVEN is a data- and parameter-efficient generative framework for implicit attribute value extraction. We introduce a novel Learning-by-Comparison technique to reduce model confusion. Our experiments reveal that EIVEN significantly outperforms existing methods in extracting implicit attribute values.
  arXiv  Detail & Related papers  (2024-04-13T03:15:56Z)
• AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction [42.79022954630978]
  Product attribute value extraction plays an important role for many real-world applications in e-Commerce such as product search and recommendation. Previous methods treat it as a sequence labeling task that needs more annotation for position of values in the product text. We propose a classification model with semantic matching and negative label sampling for attribute value extraction.
  arXiv  Detail & Related papers  (2023-10-11T02:22:28Z)
• MAVE: A Product Dataset for Multi-source Attribute Value Extraction [10.429320377835241]
  We introduce MAVE, a new dataset to better facilitate research on product attribute value extraction. MAVE is composed of a curated set of 2.2 million products from Amazon pages, with 3 million attribute-value annotations across 1257 unique categories. We propose a novel approach that effectively extracts the attribute value from the multi-source product information.
  arXiv  Detail & Related papers  (2021-12-16T06:48:31Z)
• AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding [55.89773725577615]
  We present AdaTag, which uses adaptive decoding to handle attribute extraction. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.
  arXiv  Detail & Related papers  (2021-06-04T07:54:11Z)
• Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product [40.46223408546036]
  Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. We propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images.
  arXiv  Detail & Related papers  (2020-09-15T15:10:51Z)
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach [61.2786065744784]
  In recommender systems, users and items are associated with attributes, and users show preferences to items. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
  arXiv  Detail & Related papers  (2020-05-25T10:50:01Z)
• Attribute-based Regularization of Latent Spaces for Variational Auto-Encoders [79.68916470119743]
  We present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded.
  arXiv  Detail & Related papers  (2020-04-11T20:53:13Z)
• Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition [102.45926816660665]
  We propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples. The principle lies in that attribute features are shared among fine-grained sub-categories, and can be seamlessly transferred among images.
  arXiv  Detail & Related papers  (2020-04-06T14:06:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.