{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mave-a-product-dataset-for-multi-source","title":"MAVE: A Product Dataset for Multi-source Attribute Value Extraction","arxiv_id":"2112.08663","date":"2021-12-16","proceeding":null,"authors":["Li Yang","Qifan Wang","Zac Yu","Anand Kulkarni","Sumit Sanghai","Bin Shu","Jon Elsas","Bhargav Kanagal"],"abstract":"Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, 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. MAVE has four main and unique advantages: First, MAVE is the largest product attribute value extraction dataset by the number of attribute-value examples. Second, MAVE includes multi-source representations from the product, which captures the full product information with high attribute coverage. Third, MAVE represents a more diverse set of attributes and values relative to what previous datasets cover. Lastly, MAVE provides a very challenging zero-shot test set, as we empirically illustrate in the experiments. We further propose a novel approach that effectively extracts the attribute value from the multi-source product information. We conduct extensive experiments with several baselines and show that MAVE is an effective dataset for attribute value extraction task. It is also a very challenging task on zero-shot attribute extraction. Data is available at {\\it \\url{https://github.com/google-research-datasets/MAVE}}.","url_abs":"https://arxiv.org/abs/2112.08663v1","url_pdf":"https://arxiv.org/pdf/2112.08663v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mave-a-product-dataset-for-multi-source","repo_url":"https://github.com/google-research-datasets/mave","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"attribute-extraction","task_name":"Attribute Extraction"},{"task_slug":"attribute-value-extraction","task_name":"Attribute Value Extraction"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":null,"method_name":null}],"datasets_introduced":[{"slug":"mave","name":"MAVE","full_name":"MAVE: : A Product Dataset for Multi-source Attribute Value Extraction"},{"slug":"mave-attribute-black-tea-variety","name":"MAVE - Attribute: Black Tea Variety","full_name":"MAVE - Attribute: Black Tea Variety:  A Product Dataset for Multi-source Attribute Value Extraction"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/attribute-value-extraction-on-mave","task":"Attribute Value Extraction","dataset":"MAVE","model":"MAVEQA","rank_in_archive_order":1,"of":3,"metrics":{"F1-score":"98.32"},"uses_additional_data":false},{"leaderboard":"/sota/attribute-value-extraction-on-mave","task":"Attribute Value Extraction","dataset":"MAVE","model":"AVEQA","rank_in_archive_order":2,"of":3,"metrics":{"F1-score":"98.14"},"uses_additional_data":false},{"leaderboard":"/sota/attribute-value-extraction-on-mave","task":"Attribute Value Extraction","dataset":"MAVE","model":"AD-Opentag","rank_in_archive_order":3,"of":3,"metrics":{"F1-score":"79.73"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.08663","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}