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        <title>Thiyaga6851 at SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models using Neuro-Symbolic Mapping</title>
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            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Debanjan</namePart>
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            <namePart type="given">Kai</namePart>
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    <abstract>This paper presents our system for SemEval-2026 Task 11 Subtask 1, which evaluates the formal validity of English syllogisms independently of semantic plausibility. To reduce content effects, we use a hybrid neuro-symbolic pipeline that separates natural-language abstraction from logical inference. The system maps each syllogism into categorical propositions using template rules and a learned parser, followed by explicit role mapping for the major, minor, and middle terms. If the abstraction is structurally complete, an exact Venn-style satisfiability solver checks validity; otherwise, the instance is routed to a learned fallback classifier. Our official submission achieved 71.73% accuracy, a Total Content Effect of 11.84, a Combined Score of 20.19, and a rank of 41st. Development analysis shows that symbolic inference is reliable on well-formed abstractions, while most remaining errors arise from paraphrase, multiword terms, and unstable term alignment.</abstract>
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%0 Conference Proceedings
%T Thiyaga6851 at SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models using Neuro-Symbolic Mapping
%A Pk, Thiyagarajaa
%A D., Thenmozhi
%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 pk-d-2026-thiyaga6851
%X This paper presents our system for SemEval-2026 Task 11 Subtask 1, which evaluates the formal validity of English syllogisms independently of semantic plausibility. To reduce content effects, we use a hybrid neuro-symbolic pipeline that separates natural-language abstraction from logical inference. The system maps each syllogism into categorical propositions using template rules and a learned parser, followed by explicit role mapping for the major, minor, and middle terms. If the abstraction is structurally complete, an exact Venn-style satisfiability solver checks validity; otherwise, the instance is routed to a learned fallback classifier. Our official submission achieved 71.73% accuracy, a Total Content Effect of 11.84, a Combined Score of 20.19, and a rank of 41st. Development analysis shows that symbolic inference is reliable on well-formed abstractions, while most remaining errors arise from paraphrase, multiword terms, and unstable term alignment.
%U https://aclanthology.org/2026.semeval-1.276/
%P 2187-2192

Markdown (Informal)

[Thiyaga6851 at SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models using Neuro-Symbolic Mapping](https://aclanthology.org/2026.semeval-1.276/) (Pk & D., SemEval 2026)

• Thiyaga6851 at SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models using Neuro-Symbolic Mapping (Pk & D., SemEval 2026)

ACL

• Thiyagarajaa Pk and Thenmozhi D.. 2026. Thiyaga6851 at SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models using Neuro-Symbolic Mapping. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2187–2192, San Diego, California, USA. Association for Computational Linguistics.