Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction

dc.contributor.authorGong, Jiayingen
dc.contributor.authorChen, Wei-Teen
dc.contributor.authorEldardiry, Hodaen
dc.date.accessioned2023-11-02T13:05:16Zen
dc.date.available2023-11-02T13:05:16Zen
dc.date.issued2023-10-21en
dc.date.updated2023-11-01T08:01:04Zen
dc.description.abstractExisting attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e- Commerce. Thus, we formulate AVE in multi-label few-shot learning (FSL), aiming to extract unseen attribute value pairs based on a small number of training examples. We propose a Knowledge- Enhanced Attentive Framework (KEAF) based on prototypical networks, leveraging the generated label description and category information to learn more discriminative prototypes. Besides, KEAF integrates with hybrid attention to reduce noise and capture more informative semantics for each class by calculating the label-relevant and query-related weights. To achieve multi-label inference, KEAF further learns a dynamic threshold by integrating the semantic information from both the support set and the query set. Extensive experiments with ablation studies conducted on two datasets demonstrate that our proposed model significantly outperforms other SOTA models for information extraction in few-shot learning.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1145/3583780.3615142en
dc.identifier.urihttp://hdl.handle.net/10919/116602en
dc.language.isoenen
dc.publisherACMen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderThe author(s)en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleKnowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extractionen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
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