{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:37Z","timestamp":1761176257565,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Cross-topic automated essay scoring (AES) aims to develop a transferable model capable of effectively evaluating essays on a target topic. A significant challenge in this domain arises from the inherent discrepancies between topics. While existing methods predominantly focus on extracting topic-shared features through distribution alignment of source and target topics, they often neglect topic-specific features, limiting their ability to assess critical traits such as topic adherence. To address this limitation, we propose an Adversarial TOpic-aware Prompt-tuning (ATOP), a novel method that jointly learns topic-shared and topic-specific features to improve cross-topic AES. ATOP achieves this by optimizing a learnable topic-aware prompt\u2014comprising both shared and specific components\u2014to elicit relevant knowledge from pre-trained language models (PLMs). To enhance the robustness of topic-shared prompt learning and mitigate feature scale sensitivity introduced by topic alignment, we incorporate adversarial training within a unified regression and classification framework. In addition, we employ a neighbor-based classifier to model the local structure of essay representations and generate pseudo-labels for target-topic essays. These pseudo-labels are then used to guide the supervised learning of topic-specific prompts tailored to the target topic. Extensive experiments on the publicly available ASAP++ dataset demonstrate that ATOP significantly outperforms existing state-of-the-art methods in both holistic and multi-trait essay scoring. The implementation of our method is publicly available at: https:\/\/github.com\/zhaohy777\/ATOP.<\/jats:p>","DOI":"10.3233\/faia251274","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:38Z","timestamp":1761126998000},"source":"Crossref","is-referenced-by-count":0,"title":["Adversarial Topic-Aware Prompt-Tuning for Cross-Topic Automated Essay Scoring"],"prefix":"10.3233","author":[{"given":"Chunyun","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Hongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Chaoran","family":"Cui","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Qilong","family":"Song","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Zhiqing","family":"Lu","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Shuai","family":"Gong","sequence":"additional","affiliation":[{"name":"Shandong University of Finance and Economics"}]},{"given":"Kailin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Toronto"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251274","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:56:39Z","timestamp":1761126999000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251274","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}