@inproceedings{gupta-etal-2026-0704mis,
title = "0704mis at {S}em{E}val-2026 Task 11: Single-Call Joint Abstraction for Robust Neuro-Symbolic Retrieval",
author = "Gupta, Ishita and
Goyal, Dhruv and
Bedi, Jatin",
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-1.244/",
pages = "1944--1949",
ISBN = "979-8-89176-414-9",
abstract = "Neuro-symbolic Basis for Robust Syllogistic Reasoning Under Distractors.We present our submission to SemEval-2026 Task 11 Subtasks 2 and 4, on syllogistic premise retrieval with distractors. Our system is based on a robustness-first neuro-symbolic pipeline. The key innovation is single-call joint abstraction: rather than parsing all statements independently, one LLM call jointly abstracts all premises and the conclusion into categorical logical forms (A/E/I/O) where symbolic (X/Y/Z) mappings are globally consistent. This allows reliable detection of the shared middle term needed for syllogistic validation. Parsed forms are passed through an exhaustive O(n{\texttwosuperior}) premise-pair search with deterministic validation against the 24 valid Aristotelian syllogistic forms via constant time lookup. Ablation studies show that more theoretically sophisticated variants degrade performance when logical-form extraction is the primary bottleneck. Our approach achieves competitive rankings in both English and multilingual settings while remaining simple, deterministic, and content-invariant."
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<abstract>Neuro-symbolic Basis for Robust Syllogistic Reasoning Under Distractors.We present our submission to SemEval-2026 Task 11 Subtasks 2 and 4, on syllogistic premise retrieval with distractors. Our system is based on a robustness-first neuro-symbolic pipeline. The key innovation is single-call joint abstraction: rather than parsing all statements independently, one LLM call jointly abstracts all premises and the conclusion into categorical logical forms (A/E/I/O) where symbolic (X/Y/Z) mappings are globally consistent. This allows reliable detection of the shared middle term needed for syllogistic validation. Parsed forms are passed through an exhaustive O(n²) premise-pair search with deterministic validation against the 24 valid Aristotelian syllogistic forms via constant time lookup. Ablation studies show that more theoretically sophisticated variants degrade performance when logical-form extraction is the primary bottleneck. Our approach achieves competitive rankings in both English and multilingual settings while remaining simple, deterministic, and content-invariant.</abstract>
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%0 Conference Proceedings
%T 0704mis at SemEval-2026 Task 11: Single-Call Joint Abstraction for Robust Neuro-Symbolic Retrieval
%A Gupta, Ishita
%A Goyal, Dhruv
%A Bedi, Jatin
%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 gupta-etal-2026-0704mis
%X Neuro-symbolic Basis for Robust Syllogistic Reasoning Under Distractors.We present our submission to SemEval-2026 Task 11 Subtasks 2 and 4, on syllogistic premise retrieval with distractors. Our system is based on a robustness-first neuro-symbolic pipeline. The key innovation is single-call joint abstraction: rather than parsing all statements independently, one LLM call jointly abstracts all premises and the conclusion into categorical logical forms (A/E/I/O) where symbolic (X/Y/Z) mappings are globally consistent. This allows reliable detection of the shared middle term needed for syllogistic validation. Parsed forms are passed through an exhaustive O(n²) premise-pair search with deterministic validation against the 24 valid Aristotelian syllogistic forms via constant time lookup. Ablation studies show that more theoretically sophisticated variants degrade performance when logical-form extraction is the primary bottleneck. Our approach achieves competitive rankings in both English and multilingual settings while remaining simple, deterministic, and content-invariant.
%U https://aclanthology.org/2026.semeval-1.244/
%P 1944-1949
Markdown (Informal)
[0704mis at SemEval-2026 Task 11: Single-Call Joint Abstraction for Robust Neuro-Symbolic Retrieval](https://aclanthology.org/2026.semeval-1.244/) (Gupta et al., SemEval 2026)
ACL