Autoregressive Score Generation for Multi-trait Essay Scoring
Heejin Do, Yunsu Kim, Gary Lee
Abstract
Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score.However, studies have yet to explore these models in multi-trait AES, possibly due to the inefficiency of replicating BERTbased models for each trait.Breaking away from the existing sole use of encoder, we propose an autoregressive prediction of multi-trait scores (ArTS), incorporating a decoding process by leveraging the pre-trained T5.Unlike prior regression or classification methods, we redefine AES as a score-generation task, allowing a single model to predict multiple scores.During decoding, the subsequent trait prediction can benefit by conditioning on the preceding trait scores.Experimental results proved the efficacy of ArTS, showing over 5% average improvements in both prompts and traits.