{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:42:52Z","timestamp":1780616572012,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the VIE task simply as a sequence labeling problem or classification problem, which requires models to carefully identify each kind of semantics by introducing multimodal features, such as font, color, layout. But simply introducing multimodal features can't work well when faced with numeric semantic categories or some ambiguous texts. To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities. Besides, we introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values, which helps model converge more smoothly. Comprehensive experiments demonstrate that the proposed MatchVIE can significantly outperform previous methods. Notably, to the best of our knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling the relevancy between keys and values and it is a good complement to the existing methods.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/144","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"1039-1045","source":"Crossref","is-referenced-by-count":29,"title":["MatchVIE: Exploiting  Match Relevancy between Entities  for Visual Information Extraction"],"prefix":"10.24963","author":[{"given":"Guozhi","family":"Tang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lele","family":"Xie","sequence":"additional","affiliation":[{"name":"Ant Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, China"},{"name":"Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiapeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingdong","family":"Chen","sequence":"additional","affiliation":[{"name":"Ant Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Xu","sequence":"additional","affiliation":[{"name":"Lenovo Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianying","family":"Wang","sequence":"additional","affiliation":[{"name":"Lenovo Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Lenovo Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Lenovo Research, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:01:37Z","timestamp":1628679697000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/144"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/144","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}