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    <abstract>Sentence-level structural reasoning, reflected in relational connectivity patterns induced by attention distributions, plays an important role in grammatical acceptability judgment and figurative expression understanding. However, most existing scoring approaches rely primarily on token-level probability signals or sentence-level similarity, which do not explicitly capture structural relationships between tokens. We propose Scenario-Anchored Topology-Inspired Structural Scoring (SATS), a structure-aware comparison method that evaluates coherence between a scenario and multiple candidate interpretations using connectivity patterns derived from attention matrices. Unlike prior topology-inspired approaches that focus on pairwise comparisons, SATS directly supports multi-option reasoning while maintaining low computational cost. Experiments on BLiMP, FLUTE, and Pragmatics show that SATS improves encoder-based model performance in many settings and can approach the accuracy of much larger language models. These results suggest that explicit structural comparison can provide a useful complementary signal for the evaluated language understanding tasks.</abstract>
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%0 Conference Proceedings
%T SATS : Scenario-Anchored Topology-Inspired Structural Scoring
%A Kim, Dohyeon
%A Yang, Jihoon
%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 kim-yang-2026-sats
%X Sentence-level structural reasoning, reflected in relational connectivity patterns induced by attention distributions, plays an important role in grammatical acceptability judgment and figurative expression understanding. However, most existing scoring approaches rely primarily on token-level probability signals or sentence-level similarity, which do not explicitly capture structural relationships between tokens. We propose Scenario-Anchored Topology-Inspired Structural Scoring (SATS), a structure-aware comparison method that evaluates coherence between a scenario and multiple candidate interpretations using connectivity patterns derived from attention matrices. Unlike prior topology-inspired approaches that focus on pairwise comparisons, SATS directly supports multi-option reasoning while maintaining low computational cost. Experiments on BLiMP, FLUTE, and Pragmatics show that SATS improves encoder-based model performance in many settings and can approach the accuracy of much larger language models. These results suggest that explicit structural comparison can provide a useful complementary signal for the evaluated language understanding tasks.
%U https://aclanthology.org/2026.findings-emnlp.492/
%P 10313-10332

Markdown (Informal)

[SATS : Scenario-Anchored Topology-Inspired Structural Scoring](https://aclanthology.org/2026.findings-emnlp.492/) (Kim & Yang, Findings 2026)

• SATS : Scenario-Anchored Topology-Inspired Structural Scoring (Kim & Yang, Findings 2026)

ACL

• Dohyeon Kim and Jihoon Yang. 2026. SATS : Scenario-Anchored Topology-Inspired Structural Scoring. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10313–10332, Budapest, Hungary. Association for Computational Linguistics.