NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication

Prawaal Sharma, Poonam Goyal, Navneet Goyal, Vidisha Sharma


Abstract
Digital communication has become the cornerstone of modern interaction, enabling rapid, accessible, and interactive exchanges. However, individuals with lower academic literacy often face significant barriers, exacerbating the “digital divide.” In this work, we introduce a novel, universal ideographic metalanguage designed as an innovative communication framework that transcends academic, linguistic, and cultural boundaries. Our approach leverages principles of Neuro-symbolic AI, combining neural-based large language models (LLMs) enriched with world knowledge and symbolic knowledge heuristics grounded in the linguistic theory of Natural Semantic Metalanguage (NSM). This enables the semantic decomposition of complex ideas into simpler, atomic concepts. Adopting a human-centric, collaborative methodology, we engaged over 200 semi-literate participants in defining the problem, selecting ideographs, and validating the system. With over 80% semantic comprehensibility, an accessible learning curve, and universal adaptability, our system effectively serves underprivileged populations with limited formal education.
Anthology ID:
2025.findings-emnlp.546
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2025
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10322–10340
Language:
URL:
https://aclanthology.org/2025.findings-emnlp.546/
DOI:
Bibkey:
Cite (ACL):
Prawaal Sharma, Poonam Goyal, Navneet Goyal, and Vidisha Sharma. 2025. NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 10322–10340, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication (Sharma et al., Findings 2025)
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https://aclanthology.org/2025.findings-emnlp.546.pdf
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