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    <titleInfo>
        <title>A Combinatorial Approach to Neural Emergent Communication</title>
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        <namePart type="given">Zheyuan</namePart>
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            <namePart type="given">Owen</namePart>
            <namePart type="family">Rambow</namePart>
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            <namePart type="given">Leo</namePart>
            <namePart type="family">Wanner</namePart>
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            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Barbara</namePart>
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            <namePart type="given">Steven</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Substantial research on deep learning-based emergent communication uses the referential game framework, specifically the Lewis signaling game, however we argue that successful communication in this game typically only need one or two symbols for target image classification because of a sampling pitfall in the training data. To address this issue, we provide a theoretical analysis and introduce a combinatorial algorithm SolveMinSym (SMS) to solve the symbolic complexity for classification, which is the minimum number of symbols in the message for successful communication. We use the SMS algorithm to create datasets with different symbolic complexity to empirically show that data with higher symbolic complexity increases the number of effective symbols in the emergent language.</abstract>
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%0 Conference Proceedings
%T A Combinatorial Approach to Neural Emergent Communication
%A Zhang, Zheyuan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F zhang-2025-combinatorial
%X Substantial research on deep learning-based emergent communication uses the referential game framework, specifically the Lewis signaling game, however we argue that successful communication in this game typically only need one or two symbols for target image classification because of a sampling pitfall in the training data. To address this issue, we provide a theoretical analysis and introduce a combinatorial algorithm SolveMinSym (SMS) to solve the symbolic complexity for classification, which is the minimum number of symbols in the message for successful communication. We use the SMS algorithm to create datasets with different symbolic complexity to empirically show that data with higher symbolic complexity increases the number of effective symbols in the emergent language.
%U https://aclanthology.org/2025.coling-main.112/
%P 1660-1666

Markdown (Informal)

[A Combinatorial Approach to Neural Emergent Communication](https://aclanthology.org/2025.coling-main.112/) (Zhang, COLING 2025)

• A Combinatorial Approach to Neural Emergent Communication (Zhang, COLING 2025)

ACL

• Zheyuan Zhang. 2025. A Combinatorial Approach to Neural Emergent Communication. In Proceedings of the 31st International Conference on Computational Linguistics, pages 1660–1666, Abu Dhabi, UAE. Association for Computational Linguistics.