Adversarial Topic-aware Prompt-tuning for Cross-topic Automated Essay Scoring

• URL: http://arxiv.org/abs/2508.05987v1
• Date: Fri, 08 Aug 2025 03:43:01 GMT
• Title: Adversarial Topic-aware Prompt-tuning for Cross-topic Automated Essay Scoring
• Authors: Chunyun Zhang, Hongyan Zhao, Chaoran Cui, Qilong Song, Zhiqing Lu, Shuai Gong, Kailin Liu,
• Abstract summary: Cross-topic automated essay scoring (AES) aims to develop a transferable model capable of effectively evaluating essays on a target topic.<n>We propose an Adversarial TOpic-aware Prompt-tuning (ATOP) to improve cross-topic AES.
• Score: 4.751266396409427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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--comprising both shared and specific components--to 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://anonymous.4open.science/r/ATOP-A271.

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