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Inf. Syst."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute\u2013value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text generation, whose aim is to generate coherent text for the given non-linguistic data according to a specific style. This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference and biased training samples. To address these challenges, we propose a novel stylized data-to-text generation model, named StyleD2T, comprising three components: logic planning-enhanced data embedding, mask-based style embedding, and unbiased stylized text generation. In the first component, we introduce a graph-guided logic planner for attribute organization to ensure the logic of generated text. In the second component, we devise feature-level mask-based style embedding to extract the essential style signal from the given unstructured style reference. In the last one, pseudo triplet augmentation is utilized to achieve unbiased text generation, and a multi-condition based confidence assignment function is designed to ensure the quality of pseudo samples. Extensive experiments on a newly collected dataset from Taobao\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n            have been conducted, and the results show the superiority of our model over existing methods.\n          <\/jats:p>","DOI":"10.1145\/3603374","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T07:13:34Z","timestamp":1686122014000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Stylized Data-to-text Generation: A Case Study in the E-Commerce Domain"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9827-5835","authenticated-orcid":false,"given":"Liqiang","family":"Jing","sequence":"first","affiliation":[{"name":"Shandong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5274-4197","authenticated-orcid":false,"given":"Xuemeng","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3469-1172","authenticated-orcid":false,"given":"Xuming","family":"Lin","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3519-6301","authenticated-orcid":false,"given":"Zhongzhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7914-2981","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1476-0273","authenticated-orcid":false,"given":"Liqiang","family":"Nie","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology (Shenzhen), China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324997001514"},{"key":"e_1_3_2_3_2","first-page":"9583","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Caramalau Razvan","year":"2021","unstructured":"Razvan Caramalau, Binod Bhattarai, and Tae-Kyun Kim. 2021. 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