Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring
Cong Wang, Zhiwei Jiang, Yafeng Yin, Zifeng Cheng, Shiping Ge, Qing Gu · 2023
Automated Essay Scoring (AES) aims to evaluate the quality score for input essays.In this work, we propose a novel unsupervised AES approach ULRA, which does not require groundtruth scores of essays for training.The core idea of our ULRA is to use multiple heuristic quality signals as the pseudo-groundtruth, and then train a neural AES model by learning from the aggregation of these quality signals.To aggregate these inconsistent quality signals into a unified supervision, we view the AES task as a ranking problem, and design a special Deep Pairwise Rank Aggregation (DPRA) loss for training.In the DPRA loss, we set a learnable confidence weight for each signal to address the conflicts among signals, and train the neural AES model in a pairwise way to disentangle the cascade effect among partialorder pairs.Experiments on eight prompts of ASPA dataset show that ULRA achieves the state-of-the-art performance compared with previous unsupervised methods in terms of both transductive and inductive settings.Further, our approach achieves comparable performance with many existing domain-adapted supervised models, showing the effectiveness of ULRA.The code is available at https: //github.com/tenvence/ulra.