Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers (2022)

Abstract: Languages around the world employ classifier systems as a method of semantic organization and categorization. These systems are rife with variability, violability, and ambiguity, and are prone to constant change over time. We explicitly model change in classifier systems as the population-level outcome of child language acquisition over time in order to shed light on the factors that drive change to classifier systems. Our research consists of two parts: a contrastive corpus study of Cantonese and Mandarin child-directed speech to determine the role that ambiguity and homophony avoidance may play in classifier learning and change followed by a series of population-level learning simulations of an abstract classifier system. We find that acquisition without reference to ambiguity avoidance is sufficient to drive broad trends in classifier change and suggest an additional role for adults and discourse factors in classifier death.

## introduction classifier and measure word systems are common across the world's languages. while they are the most common and most associated with southeast and east asia, they are also present in some languages of south asia, australia, the pacific, and the americas among others @xcite . systems vary language-to-language, but share some general properties. they divide up the space of nouns along some semantic space, often encoding lexical semantic information including animacy, concreteness, and size and shape categories. for example, mandarin has classifiers for long objects (e.g., tiáo 條), some animals (zhī 隻), and vehicles (liàng 輛). on the other hand, some classifiers like the mandarin general classifier gè 個 do not seem to pick out anything in particular, or they instead pick out extremely narrow, almost lexicalized classes, such as zūn 尊, which as a classifier applies only to certain colossal metal objects such figure 1 : the z-model of change extended to a population setting as cannons and buddhist statues @xcite . compared to most inflectional noun class systems, classifiers are more subject to variable discourse conditions. several classifiers may be used grammatically with a given noun as conditions allow. for example, 'a goat' may be expressed with the animal classifier zhī or general classifier gè, but also tiáo or tóu 頭 used for livestock @xcite . the balance of semantic specificity, arbitrariness, and variability presents a challenge for native learners. how do individuals acquire both the semantic conditions and arbitrary lexical patterns of classifier systems? parallel to this, classifier systems are subject to constant change, both for language-internal reasons (e.g., grammaticalization of new classifiers, word death of old classifiers) and external ones, particularly contact @xcite . @xcite illustrates a few cases of changes in classifier usage in mandarin and its ancestors over the past 3500 years. gè 個, the overwhelming majority catch-all classifier in the modern language only gained this status during the qing dynasty @xcite . for the millennium prior since the tang dynasty, méi 枚 had been the default, but it has since been relegated to a niche classifier for small needle and badge-like objects. both gè and méi began as niche classifiers in their respective eras before gradually generalizing. in a similar vein, habibi et al. (2020) explore how linguistic categories change through chaining, via the usage of mandarin chinese classifiers in the past half century. the latter two studies discuss the development of mandarin classifiers over time. they are based on careful research, but they are also limited to a single language. @xcite in particular stops short of a quantitative assessment. we provide a computational analysis of diachronic trends in classifier systems which complements prior developmental and historical research. we approach the problem in two ways. first, we present a quantitative analysis of classifiers in cantonese and mandarin child-directed speech to investigate the possibility of a functional role for classifiers as disambiguators which could influence the direction of child-driven change. second, we model a simulated classifier system using a population-level transmission model to determine how language acquisition may drive trends in classifier patterns over time. we find support for input sparsity and learning, without reference to specific functional concerns, as a primary driver for gradual classifier generalization over time. ## classifier learning and change language acquisition has long been implicated as a driver of language change @xcite @xcite @xcite @xcite @xcite @xcite , and this has particularly been true for morphology, where child overproductivity errors @xcite quite often mirror the processes of analogical change, which is itself closely connected to productivity @xcite . classifier systems are not structurally morphological and do not trigger syntactic agreement like inflectional noun class systems, but they share some key properties in both their use and acquisition. both often encode lexical semantic information including animacy, concreteness, and size and shape categories. for example, the bantu language shona has noun classes for mostly long-skinny things (e.g., class 11 ru-), classes for animals (e.g., class 9 (i)-), and miscellaneous classes (e.g., class 7 chi-) which correspond broadly to the mandarin classifiers described in section 1. both noun classes and classifiers may be semantically porous with many lexical exceptions. and while classifiers are generally more variable than inflectional classes, the later may also show variability. in shona again, people usually take the class 1 mu-prefix (mu-nhu 'person'), but if a speaker wishes to highlight that a person is particularly tall and thin, they may employ the long-skinny class 11 prefix (ru-nhu). learners of classifier languages exhibit generally competent classifier use by age 4 or 5, though they show some command over their syntax much earlier @xcite @xcite . children are prone to overusing the general or default classifier in japanese @xcite , mandarin @xcite , cantonese @xcite , and vietnamese @xcite , similar to the over-extension of default patterns in morphology @xcite . they take longer to acquire rare classifiers and those with complex semantic restrictions @xcite . a division of classifiers into semantically welldefined and arbitrary features is well-motivated by a series of experiments carried out by @xcite on mandarin. this further clarifies what the learning task entails. children must work out whether classifiers are lexically defined or apply generally to a given semantic class and is consistent with observed developmental trajectories: young learners pass through an early lexicalized stage in which classifiers are defined narrowly by which lexical items they match with rather than their general semantics. this is by a higher than adult-rate use of generic classifiers, before they settle on an adult-like distribution @xcite . this is parallel to the classic inflectional learning trajectory, a pre-generalization period, followed by over-generalization of defaults, followed by settling on an adult-like distribution. @xcite explicitly connects classifier acquisition to change in chinese and notes several parallels between chinese classifier acquisition and change. most relevant for the present study, classifiers are narrowly, perhaps lexically, defined when they enter the language and then trend towards generality. furthermore, they apply to concrete objects with real-world identifiable semantics before abstract concepts, in line with children's preference for real world referents in their dialogues. taken together, classifier systems have enough in common with inflectional class systems that their acquisition and change can be modeled similarly. linguistic transmission, the passing of a language from one generation to the next through native language acquisition @xcite ), provides a fundamental role for acquisition in change. @xcite formalizes change as the long-term consequence of abductive processes in language acquisition through his z-model: speakers have some internal grammar which generates a set of linguistic examples which serve as the input to the next generation. the next generation acquires a grammar based on these finite inputs and produces outputs for the next generation. this process proceeds indefinitely. abduction is error-prone, and differences between the grammars of the first and second generation are tantamount to change. but language change is fundamentally a population-level process @xcite , so the z-model must be thought of as countless parallel lines of transmission and not a single z-shape. additionally, transmission does not proceed through discrete generations, but rather is continuous across age cohorts in the population, so the z-model should be staggered both across the population and across time. this view, diagrammed in figure 1 , forms the conceptual basis of our simulation. a population-based transmission model in which what is acquired is driven primarily by the input and not additional functional factors may be described as neutral. this is often assumed as the baseline in biological evolution @xcite , and may be relevant for language change as well @xcite . the following section tests an alternative, that classifiers emerge to decrease homophony, before adopting a neutral approach. ## classifiers and homophony this section quantifies classifier use in mandarin and cantonese child-directed speech (cds). their systems are quite similar, both having descended from middle chinese. since their divergence, the languages have undergone substantial phonological divergence resulting in much less syllable diversity in mandarin compared to cantonese. foot_0 for this reason, mandarin is expected to show more homophony than cantonese, though this is offset by an increase in polysyllabic words in mandarin. disambiguation of homophones is one possible function of classifiers and a potential functional (i.e., non-neutral) driver of change. more elaborate classifier systems may develop in response to more rampant homophony. we compare mandarin and cantonese cds to determine whether homophony avoidance is plausibly part of the child's role in the development of the chinese classifier systems. if true, we would expect mandarin cds to show more noun form ambiguity than cantonese and show more classifier disambiguation of homophonous word types. for comparison, we also investigated the rate of polysyllabic noun forms in mandarin and cantonese. the increase in polysyllabicity in chinese varieties is traditionally taken to be a response to increased homophony due to phonemic mergers @xcite . all pos-tagged mandarin and cantonese corpora were extracted from the r conversion @xcite of the childes database of child-directed speech corpora (macwhinney, 2000) except for erbaugh, which could not be retrieved. the first two data rows of since corpus size could have a substantial effect on the ratios reported in the corpora, we opted to downsample the mandarin corpus to match the size of cantonese and compare both the downsampled and raw mandarin. we dropped out mandarin tokens selected uniformly at random until the corpus matched the cantonese corpus in type or token count. this was repeated for 100 trials and the counts for each trial were averaged. the resulting mandarin type matched for type count and mandarin tok matched for token count are the last two rows in the table also shows the rates of homophonous word types in the corpora as well as the proportion of those which are disambiguated. we defined a homophonous word type as disambiguated if every homophone is attested with at least one classifier not attested with any other homophone in a set, and a disambiguated word token as any token which belongs to a disambiguated word type. despite the increase in polysyllabicity, mandarin is still much more ambiguous than cantonese. nevertheless, its homophones are not significantly more disambiguated. 2 this analysis is consistent with (but does not prove) the idea that polysyllabicity emerged in chinese in a response to ambiguity. in contrast, it does not support a role for homophony avoidance in adults as a motivation for the classifier system. even though the mandarin acquisition corpora attest more classifiers and measure words, only about 1/5 of homophonous types and 1/18 of homophonous tokens are disambiguated by classifiers. the fact that tokens are much less likely than types to be disambiguated, and that the type disambiguation rate declines as the number of types fall in ## a classifier system in a population the empirical analysis in the previous section motivates a neutral model of change for the chinese classifier system. in this section, we introduce a population-level model of linguistic transmission to investigate the dynamics of classifier systems over time. we describe the details of our simulation, including the algorithm and parameters, their relevance, and their specific empirical motivations. we then discuss our findings across different parameter settings, and consider their implications in the study of classifiers, learning, and language change. ## discussion and conclusion in this paper, we advocate for a view of language change as a natural outcome of language acquisition over time and across a population. this acquisition-driven view of change provides insight into the long-term dynamics of classifier systems through a cross-linguistic corpus study of modern chinese child-directed speech and a populationlevel simulation of classifier change. the cross-linguistic study (section 3) contrasts mandarin and cantonese, two closely related but not mutually intelligible languages with a recent common ancestor, to test the hypothesis that classifier use is driven by homophony avoidance. we found that though mandarin child-directed speech has substantially more homophonous types than cantonese, its classifiers actually disambiguate homophones significantly less often. this is contrasted with polysyllabicity in mandarin, which does show a trend consistent with homophony avoidance. this result motivates a neutral model of classifier change driven by matters of learning and input sparsity not primarily concerned with functional pressures. we apply the tolerance principle (tp), a model of productivity learning, to our populationlevel simulation and observe general trends. the tp was chosen because it successfully models ushaped learning trajectories in morphology where learners develop through memorization to overgeneralizing phases. this is similar to the developmental pattern observed in classifier learning. children begin by memorizing classifiers and the nouns they apply to, then move to over-use of general classifiers. a similar trend towards generalization is observed empirically in the history of chinese classifiers. new classifiers are specific when they are introduced and tend towards generality over time. this is not a lockstep relationship along the lines of "ontogeny recapitulates phylogeny," but two parallel trends which emerge independently from the same learning process. our populationlevel simulation of tp learners (section 4) achieves this pattern under a wide range of parameter settings, providing support for the role of learning and neutral processes in this change.