The retrieved scientific literature contains direct disagreement, with some meta-analyses and reference sources supporting the equi-complexity hypothesis of natural languages, while newer quantitative and information-theoretic studies challenge the idea.
Abstract In linguistics, there is little consensus on how to define, measure, and compare complexity across languages. We propose to take the diversity of viewpoints as a given, and to capture the complexity of a language by a vector of measurements, rather than a single value. We then assess the statistical support for two controversial hypotheses: the trade-off hypothesis and the equi-complexity hypothesis. We furnish meta-analyses of 28 complexity metrics applied to texts written in overall 80 typologically diverse languages. The trade-off hypothesis is partially supported, in the sense that around one third of the significant correlations between measures are negative. The equi-complexity hypothesis, on the other hand, is largely confirmed. While we find evidence for complexity differences in the domains of morphology and syntax, the overall complexity vectors of languages turn out virtually indistinguishable.
AbstractComplexity trade-offs are often considered as evidence for the hypothesis that all languages are equally complex; simplicity in one component of grammar is balanced by complexity in another. According to Shosted (2006), this "negative correlation hypothesis", as he calls it, was never validated using quantitative methods. The present paper recalls, in a first step, our previously found significant negative cross-linguistic correlations between syllable complexity and number of syllables per clause and per word, as well as an almost significant negative correlation between syllable complexity and number of morphological cases. All these correlations indicate complexity trade-offs between subsystems of language, as do the positive correlations found between syllable complexity, number of syllable types, and number of monosyllabic words. In a second step we argue against the view of such complexity trade-offs as proof of the equal complexity hypothesis. This hypothesis is hardly testable for several reasons: As long as it is impossible to quantify the overall complexity of a single language, it is also impossible to compare different languages with respect to that quantity. Secondly, it could – because of its character as a null hypothesis – never be corroborated for principal reasons.
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Abstract One of the fundamental questions about human language is whether all languages are equally complex. Here, we approach this question from an information-theoretic perspective. We present a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6500 different documents as represented in 41 multilingual text collections consisting of ~ 3.5 billion words or ~ 9.0 billion characters and covering 2069 different languages that are spoken as a native language by more than 90% of the world population. We statistically infer the entropy of each language model as an index of what we call average prediction complexity. We compare complexity rankings across corpora and show that a language that tends to be more complex than another language in one corpus also tends to be more complex in another corpus. In addition, we show that speaker population size predicts entropy. We argue that both results constitute evidence against the equi-complexity hypothesis from an information-theoretic perspective.
Abstract The Complexity Trade-off Hypothesis suggests that when one language domain becomes more complex, another tends to simplify to maintain an overall balance of complexity in languages. Previous studies sought to test such trade-offs across languages, implying the equal complexity across languages. However, this assumption has been increasingly questioned. Furthermore, little attention has been given to diachronic changes and interactions in lexical and syntactic complexities, especially within ancient and non-European languages. Against this backdrop, this study explored the evolution of lexical and syntactic complexities and their possible trade-off in Classical Chinese over two millennia. Based on entropy and dependency distance metrics, we found a marked increase in lexical complexity alongside a decrease in syntactic complexity. Our findings provided support to the existence of trade-offs in an individual language and broadened the scope of the complexity trade-off analysis in historical language contexts.
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