@inproceedings{wang-etal-2026-dpres,
title = "{DPRES}: Dual-Perspective Decoupled Representation Learning Framework for Multi-Trait Essay Scoring",
author = "Wang, Jiong and
Wu, Hao and
Yu, Shengquan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.617/",
pages = "12978--12990",
ISBN = "979-8-89176-462-0",
abstract = "Multi-trait automated essay scoring extends holistic scoring by evaluating essays across multiple writing dimensions. In this framework, inter-trait consistency signals provide an effective way to improve prediction across traits. However, learning shared scoring signals may obscure trait-specific features, while enforcing consistency across traits can introduce negative transfer, hindering the model{'}s ability to distinguish individual traits. To solve these issues, we propose DPRES, a dual-perspective decoupled representation learning framework that disentangles consistency and specificity at both the feature and optimization levels. Specifically, we first propose a residual subspace decomposition strategy that enforces orthogonality between shared scoring features and trait-specific features, thereby disentangling shared scoring signals from trait-specific scoring signals in the latent representation space and enhancing the model{'}s ability to capture trait-specific scoring features. Based on the subspace decomposition strategy, we further design a consistency{--}specificity decoupled learning strategy to simultaneously preserve inter-trait correlations and enhance trait-level discrimination. Experiments on public datasets demonstrate that the proposed method consistently outperforms strong baselines."
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<abstract>Multi-trait automated essay scoring extends holistic scoring by evaluating essays across multiple writing dimensions. In this framework, inter-trait consistency signals provide an effective way to improve prediction across traits. However, learning shared scoring signals may obscure trait-specific features, while enforcing consistency across traits can introduce negative transfer, hindering the model’s ability to distinguish individual traits. To solve these issues, we propose DPRES, a dual-perspective decoupled representation learning framework that disentangles consistency and specificity at both the feature and optimization levels. Specifically, we first propose a residual subspace decomposition strategy that enforces orthogonality between shared scoring features and trait-specific features, thereby disentangling shared scoring signals from trait-specific scoring signals in the latent representation space and enhancing the model’s ability to capture trait-specific scoring features. Based on the subspace decomposition strategy, we further design a consistency–specificity decoupled learning strategy to simultaneously preserve inter-trait correlations and enhance trait-level discrimination. Experiments on public datasets demonstrate that the proposed method consistently outperforms strong baselines.</abstract>
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%0 Conference Proceedings
%T DPRES: Dual-Perspective Decoupled Representation Learning Framework for Multi-Trait Essay Scoring
%A Wang, Jiong
%A Wu, Hao
%A Yu, Shengquan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-dpres
%X Multi-trait automated essay scoring extends holistic scoring by evaluating essays across multiple writing dimensions. In this framework, inter-trait consistency signals provide an effective way to improve prediction across traits. However, learning shared scoring signals may obscure trait-specific features, while enforcing consistency across traits can introduce negative transfer, hindering the model’s ability to distinguish individual traits. To solve these issues, we propose DPRES, a dual-perspective decoupled representation learning framework that disentangles consistency and specificity at both the feature and optimization levels. Specifically, we first propose a residual subspace decomposition strategy that enforces orthogonality between shared scoring features and trait-specific features, thereby disentangling shared scoring signals from trait-specific scoring signals in the latent representation space and enhancing the model’s ability to capture trait-specific scoring features. Based on the subspace decomposition strategy, we further design a consistency–specificity decoupled learning strategy to simultaneously preserve inter-trait correlations and enhance trait-level discrimination. Experiments on public datasets demonstrate that the proposed method consistently outperforms strong baselines.
%U https://aclanthology.org/2026.findings-emnlp.617/
%P 12978-12990
Markdown (Informal)
[DPRES: Dual-Perspective Decoupled Representation Learning Framework for Multi-Trait Essay Scoring](https://aclanthology.org/2026.findings-emnlp.617/) (Wang et al., Findings 2026)
ACL