无需数据自动生成语言，揭示词的涌现机制
Self-Organizing Language
- 通过自组织动态构建连续学习的可寻址记忆
- 无需标注数据即可生成符合人类语言结构的语言
- 为语言起源提供神经符号融合的新范式
我们提出一种新兴的局部记忆范式，是一种持续学习、完全并行的内容寻址记忆，能够编码全局秩序。该模型展示了无协调学习的局部约束如何产生拓扑保护的记忆，实现自发的符号秩序。因此，它构成了一种神经符号桥梁。该模型还能利用自身自组织动力学，在无数据情况下生成人类语言。这表明词汇是符号秩序演化的副产品，而人类语言在各结构层次上的模式反映了普遍的词形成机制（该机制为次正则）。本工作回答了关于所有人类语言数据存在与起源的根本问题。
原文摘要 · Abstract (English)
We introduce a novel paradigm of emergent local memory. It is a continuous-learning completely-parallel content-addressable memory encoding global order. It demonstrates how local constraints on uncoordinated learning can produce topologically protected memories realizing emergent symbolic order. It is therefore a neuro-symbolic bridge. It further has the ability to produce human language without data, by exploiting its own self-organizing dynamics. It teaches us that words arise as a side-effect of emergent symbolic order, and that human language patterns at all structural levels reflect a universal mechanism of word formation (which is subregular). This work answers essential questions about the existence \& origin of all the human language data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。