Aspect-Term Sentiment Analysis (ATSA) in multi-aspect sentences faces a fundamental tradeoff between efficiency and expressiveness. Existing models either re-encode the sentence for each aspect or rely on static use of deep representations, leading to redundant computation and limited adaptivity. We argue that Transformer depth is a costly, queryable resource, and propose DABS, a single-pass inference framework that encodes each sentence once to construct a reusable, depth-ordered substrate. Each aspect then queries this shared representation to selectively read relevant tokens and abstraction levels, without re-encoding. This decouples shared sentence encoding from lightweight, aspect-conditioned readout. Experiments on four ATSA benchmarks show that DABS achieves competitive performance while reducing end-to-end computation by up to 60% in multi-aspect settings (M >= 2). Further analyses indicate that adaptive depth querying is most beneficial for linguistically complex cases such as negation and contrast. Code is publicly available at https://github.com/panzhzh/acl-dabs
@article{arxiv.2605.20998,
title = {Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis},
author = {Yan Xia and Zhuangzhuang Pan and Amirrudin Kamsin and Chee Seng Chan},
journal= {arXiv preprint arXiv:2605.20998},
year = {2026}
}
Comments
Accepted at ACL2026 (main). Our solution (DABS) reads the sentence once, then lets each aspect selectively query the right tokens and Transformer depths, cutting redundant computation while preserving ATSA accuracy