提出新方法提升神经网络通信的符号复杂度，让语言更丰富。
A Combinatorial Approach to Neural Emergent Communication
- 用组合算法求解通信所需的最少符号数
- 高符号复杂度数据使语言中有效符号增多
- 适合研究语言演化与通信机制的学者
基于深度学习的涌现通信研究多采用参照游戏框架（特别是Lewis信号游戏），但现有方法在训练数据采样上存在缺陷，导致通信成功只需一两个符号即可完成图像分类任务。为此，本文开展理论分析，并提出组合算法SolveMinSym（SMS），用于求解分类任务下的最小符号数（即符号复杂度）。利用该算法构建不同符号复杂度的数据集，实验表明：更高符号复杂度的数据能显著增加涌现语言中的有效符号数量，从而推动更丰富的通信模式形成。
原文摘要 · Abstract (English)
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.
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