The paper explores how the human natural language structure can be seen as a
product of evolution of inter-personal communication code, targeting
maximisation of such culture-agnostic and cross-lingual metrics such as
anti-entropy, compression factor and cross-split F1 score. The exploration is
done as part of a larger unsupervised language learning effort, the attempt is
made to perform meta-learning in a space of hyper-parameters maximising F1
score based on the "ground truth" language structure, by means of maximising
the metrics mentioned above. The paper presents preliminary results of
cross-lingual word-level segmentation tokenisation study for Russian, Chinese
and English as well as subword segmentation or morphological parsing study for
English. It is found that language structure form the word-level segmentation
or tokenisation can be found as driven by all of these metrics, anti-entropy
being more relevant to English and Russian while compression factor more
specific for Chinese. The study for subword segmentation or morphological
parsing on English lexicon has revealed straight connection between the
compression been found to be associated with compression factor, while,
surprising, the same connection with anti-entropy has turned to be the inverse.

本文探讨了人类自然语言结构如何成为相互之间交流编码演化的产物，以最大化文化不可知和跨语言度量，如反熵，压缩因子和交叉分割 F1 得分，并在超参数空间中执行元学习，通过最大化上述度量，实现自然语言学习。文中介绍了针对俄语，英语和汉语的跨语言单词级分割分词研究，以及对英语的子词分割或形态分析研究的初步结果。研究发现，从分词到分词中可以发现，这些度量驱动着语言结构，反熵更相关于英语和俄语，而压缩因子更适用于中国。对于英语词汇表的子词分割或形态分析的研究揭示了压缩和压缩因子之间存在直接联系，而惊讶的是，与反熵的相同联系却变成了相反。