Adaptive Communication: Languages with More Non-Native Speakers Tend to Have Fewer Word Forms

Abstract

Explaining the diversity of languages across the world is one of the central aims of typological, historical, and evolutionary linguistics. We consider the effect of language contact-the number of non-native speakers a language has-on the way languages change and evolve. By analysing hundreds of languages within and across language families, regions, and text types, we show that languages with greater levels of contact typically employ fewer word forms to encode the same information content (a property we refer to as lexical diversity). Based on three types of statistical analyses, we demonstrate that this variance can in part be explained by the impact of non-native speakers on information encoding strategies. Finally, we argue that languages are information encoding systems shaped by the varying needs of their speakers. Language evolution and change should be modeled as the co-evolution of multiple intertwined adaptive systems: On one hand, the structure of human societies and human learning capabilities, and on the other, the structure of language.

The Entropy of Words—Learnability and Expressivity across More Than 1000 Languages

“…For example, several recent studies engage in establishing information-theoretic and corpus-based methods for linguistic typology, i.e. classifying and comparing languages according to their information encoding potential [10,[12][13][14][15][16], and how this potential evolves over time [17][18][19]. Similar methods have been applied to compare and distinguish non-linguistic sequences from written 2 of 34 language [20,21], though it is controversial whether this helps with more fine-grained distinctions between symbolic systems and written language [22,23].…”

Indian English Evolution and Focusing Visible Through Power Laws

“…Rank frequency research has expanded beyond a narrow focus on adult, monolingual, native speakers to demonstrate distinct rank frequency distributions for corpora of varying levels of L2 proficiency across users of natural language [30,31] and artificial command languages [32], L1 attritors who have lost proficiency in their L1 over their lifespan [31], different language combinations of spontaneous codeswitching [33], and in languages with varying proportions of non-native speakers [34].…”

“…Distinctive patterns have also been found in child vs. adult caregiver speech [36]: children have a lower α exponent compared to fully proficient adults. Collectively, rank frequency analysis of diachronic and comparative proficiency language data suggest that the exponent serves as an indicator of linguistic complexity [31,[34][35][36][37]. Comparisons of the exponent across multiple languages which vary in their degree of synthetic to analytic structural complexity also support the relationship of the exponent to typological differences and/or to processes of typological change [33,[37][38][39].…”

“…WE data offers an ideal testing ground for this. Specifically, because ZL and ML are quantitative measures of lexical diversity, with an inverse relationship, lower diversity should manifest through higher constants (C, α, β) [34]. Comparing parallel synchronic corpora, languages with a larger proportion of L2 speakers have higher constants, and less lexical diversity: adult L2 learners provoke a reduction in the number of word forms in the code diachronically [34].…”

“…Specifically, because ZL and ML are quantitative measures of lexical diversity, with an inverse relationship, lower diversity should manifest through higher constants (C, α, β) [34]. Comparing parallel synchronic corpora, languages with a larger proportion of L2 speakers have higher constants, and less lexical diversity: adult L2 learners provoke a reduction in the number of word forms in the code diachronically [34]. In tandem, languages with more synthetic properties (more inflected forms of the same dictionary entries and less reliance on discrete function words to show grammatical properties) display lower constants, and hence greater lexical diversity [18].…”