@inproceedings{ksiezniak-2026-team,
title = "Team ewelinaksiez at {S}em{E}val-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction",
author = "Ksi{\k{e}}{\.z}niak, Ewelina",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.272/",
pages = "2149--2154",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our system for SemEval-2026 Task{\textasciitilde}11 Subtask{\textasciitilde}1 on content-independent syllogistic reasoning. The task evaluates whether language models can determine the formal validity of logical arguments independently of their semantic plausibility. To reduce content-driven biases, we propose a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words while preserving logical structure. Experiments based on fine-tuning microsoft/deberta-large-mnli show that abstraction-based augmentation reduces Content Effect and improves accuracy, leading to competitive performance on the official leaderboard. However, we observe substantial sensitivity to random initialization, suggesting that evaluation outcomes are partly influenced by stochastic factors. To better understand these effects, we conduct a layer-wise probing analysis using a Minimum Description Length framework, showing that the proposed approach decreases the accessibility of plausibility information in later transformer layers, indicating a shift toward more structure-oriented reasoning."
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<abstract>This paper presents our system for SemEval-2026 Task~11 Subtask~1 on content-independent syllogistic reasoning. The task evaluates whether language models can determine the formal validity of logical arguments independently of their semantic plausibility. To reduce content-driven biases, we propose a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words while preserving logical structure. Experiments based on fine-tuning microsoft/deberta-large-mnli show that abstraction-based augmentation reduces Content Effect and improves accuracy, leading to competitive performance on the official leaderboard. However, we observe substantial sensitivity to random initialization, suggesting that evaluation outcomes are partly influenced by stochastic factors. To better understand these effects, we conduct a layer-wise probing analysis using a Minimum Description Length framework, showing that the proposed approach decreases the accessibility of plausibility information in later transformer layers, indicating a shift toward more structure-oriented reasoning.</abstract>
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%0 Conference Proceedings
%T Team ewelinaksiez at SemEval-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction
%A Księżniak, Ewelina
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F ksiezniak-2026-team
%X This paper presents our system for SemEval-2026 Task~11 Subtask~1 on content-independent syllogistic reasoning. The task evaluates whether language models can determine the formal validity of logical arguments independently of their semantic plausibility. To reduce content-driven biases, we propose a data augmentation strategy that progressively abstracts lexical semantics by replacing content words with symbolic placeholders and pseudo-words while preserving logical structure. Experiments based on fine-tuning microsoft/deberta-large-mnli show that abstraction-based augmentation reduces Content Effect and improves accuracy, leading to competitive performance on the official leaderboard. However, we observe substantial sensitivity to random initialization, suggesting that evaluation outcomes are partly influenced by stochastic factors. To better understand these effects, we conduct a layer-wise probing analysis using a Minimum Description Length framework, showing that the proposed approach decreases the accessibility of plausibility information in later transformer layers, indicating a shift toward more structure-oriented reasoning.
%U https://aclanthology.org/2026.semeval-1.272/
%P 2149-2154
Markdown (Informal)
[Team ewelinaksiez at SemEval-2026 Task 11: Reducing Content Bias in Syllogistic Reasoning via Semantic Abstraction](https://aclanthology.org/2026.semeval-1.272/) (Księżniak, SemEval 2026)
ACL