Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
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Computer Science > Computation and Language
Title:Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
Abstract: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 this https URL
| 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 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.20998 [cs.CL] |
| (or arXiv:2605.20998v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20998
arXiv-issued DOI via DataCite (pending registration)
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