M andarin classifier systems optimize to accommodate communicative pressures (2023)

Abstract: Previous work on noun classification implies that gender systems are inherently optimized to accommodate communicative pressures on human language learning and processing (Dye. et al 2017, 2018). They state that languages make use of either grammatical (e.g., gender) or probabilistic (pre-nominal modifiers) to smoothe the entropy of nouns in context. We show that even languages that are considered genderless, like Mandarin Chinese, possess a noun classification device that plays the same functional role as gender markers. Based on close to 1M Mandarin noun phrases extracted from the Leipzig Corpora Collection (Goldhahn et al. 2012) and their corresponding fastText embeddings (Bojanowski et al. 2016), we show that noun-classifier combinations are sensitive to same frequency, similarity, and co-occurrence interactions that structure gender systems. We also present the first study of the effects of the interaction between grammatical and probabilisitic noun classification.

## introduction the world's languages make use of a variety of strategies to classify nouns into sub-classes. while gendered languages like spanish or german typically split nouns across two (masculine m vs. feminine f) or three (m, f and neuter n) classes, other languages, e.g., in the bantu family, may split nouns across more than 10 different classes @xcite . the recurring fascination with gender systems that can be found in the linguistic literature lies in the perceived arbitrariness of gender classes, which, among other things, makes them notoriously difficult to acquire for second language learners: for example, in french or spanish the word for sun (fr: 'soleil', es: 'sol') will be m and the word for moon (fr: 'lune', es: 'luna') will be f, while in german the genders are reversed ('sonne' f, 'mond' m) and in russian 'солнце' sólnce sun would be n and moon 'луна' luná f. yet, gender systems do not appear to present any particular challenges in first language acquisition @xcite , indicating that gender systems are in fact, learnable. as speakers, humans are confronted with two main challenges, which reflect what @xcite refer to as 'competing communicative pressures': the first is learning their language, for which regularities across similar words (or structures) are the most optimal principle of organization; the second is using their language to efficiently communicate with others, for which enhanced discriminability of words and structures provides the most optimal principle. irregularities make words and structures more item specific and thus increase their discriminability -which allows for faster and more efficient communication. a fully regular system would not be optimized for communication, whereas a fully irregular one would make a language extremely difficult if not impossible to learn. however, not all parts of the system present the same challenge for learning: highly frequent words and structures are typically encountered early by learners and constantly reinforced. once they are acquired, learning is no longer necessary. irregularities in early acquired words only present a minor challenge towards the beginning of human language development, but do not pose any problems later in life. their discriminative properties do however provide significant benefits for language processing, making communication faster and more efficient. fast discrimination is especially useful for words and structures that speakers use most frequently. infrequent words and structures, on the other hand, are typically only encountered late in life, if at all. even proficient adult speakers need to leverage their knowledge about related words and structures when they first encounter them. regularity across similar structures is essential for low frequency words and structures. at the same time, faster processing of rarer structures is not as crucial to increasing overall communicative efficiency. recent work by @xcite showed that gender assignment tends not to be as arbitrary as it would appear, but instead correlates with nouns' semantics. earlier work by @xcite and @xcite had also already shown that gender systems are inherently optimized to accommodate communicative pressures on human language learning and processing via interactions between semantic similarity, frequency, and cooccurrence likelihood: since across all languages, nouns tend to be the syntactic category with the most diverse number of items @xcite , syntactic processing of nouns tends to correspond to a locus of heightened cognitive effort. this is also reflected in the fact that nouns are the most frequent sites for disfluencies, incorrect retrieval, and mishearings @xcite . gender classes that make use of gendered determiners reduce the uncertainty about upcoming nouns in context by restricting the subset of possible nouns to the ones that are consistent with the gender indicated by the determiner. @xcite show that the existence of interactions between nouns' semantics, their frequencies, and their assignment to gender classes closer reflects a cognitively optimized system than an arbitrary one. dye et al.'s 2017a paper 'cute little puppies and nice cold beers: an information theoretic analysis of prenominal adjectives' further illustrates how languages without gender markers, such as english, make use of a probabilistic counterpart to grammatical noun classification, where a larger number of pre-nominal modifiers progressively reduces the uncertainty of nouns in context. according to the authors, languages can make use of either grammatical (e.g., gender) or probabilisitic (prenominal modifier sequences) to smoothe the uncertainty (entropy @xcite ) of nouns in context. our study focuses on another, different, noun classification system which is based on nominal classifiers. it relates its structure to its cognitive processability and learnability. as a grammatical category, classifiers constitute one of the most prominent features of east and south east asian languages, such as mandarin @xcite . classifiers categorize nouns based on distinctive semantic properties such as shape, animacy, humanness, among others @xcite . for example, classifiers, such as 张 zhāng and 条 tiáo, are typically associated with different types of referents; 张 zhāng with wide and flat items like beds, nets, and maps, and 条 tiáo with long and narrow objects like rivers, scarfs, and legs. yet, just like gendered determiners, classifiers can also be seen as a grammatical means for classifying nouns into different noun classes: mandarin classifiers are part of the noun phrase. they typically appear before nouns. according to @xcite , they are obligatorily present after numerals or demonstratives: for instance, in phrases like 两 liǎng *(张 zhāng) 桌子 zhuōzi 'two tables' and 这 zhè *(条 tiáo) 椅子 yǐzi 'this bench', the classifiers (highlighted in bold) cannot be omitted. nouns will only combine with certain subsets of classifiers, but not others. just like gender markers, combining a noun with the wrong classifier would be perceived as ungrammatical. since knowing a noun's classifier appears just as crucial to correctly speaking mandarin as knowing a noun's gender would be required for german or spanish, previous studies have investigated the relation between a noun's meaning and the classifiers it combines with. @xcite show that classifiers cannot be fully predicted from the semantics of their nouns. diverse nouns such as benches, smoke, passwords, and even abstract entities like regulations can all be quantified with the same classifier 条 tiáo. however, semantically more similar nouns like bench and table do not share classifiers (条 tiáo for bench, but 张 zhāng for table). the challenges they present for second language learners are thus very similar to those encountered by learners of gender systems @xcite . on a functional level, @xcite show that classifiers also substantially reduce the conditional entropy of upcoming nouns in mandarin processing. this indicates that knowledge of a pre-nominal classifier can help speakers predict upcoming nouns. lau and grüter (2015) and grüter et al. (2020) also report that speakers use classifierreferent associations to predict upcoming nouns in mandarin, thus facilitating noun processing. while classifiers appear to share a great number of properties with gendered determiners, they also differ from gender markers in a number of significant aspects: (i) they do not operate over a one-toone correspondence with specific nouns: a given noun will typically combine with multiple classifiers, of which subsets are in turn also combined with other subsets of nouns (examples of those associations are given in the goal of our study is to investigate the structure and cognitive function of classifier systems compared to better known gender systems. in particular, we seek to understand how a system that is neither fully categorical (like gender systems where a noun is only ever assigned one gender) nor fully probabilistic (adjective-noun combinations of which none would be strictly ungrammatical) handles noun class membership and makes use of classifier-noun combinatorics to accommodate competing communicative pressures of learning and processing language @xcite . a fully regular classifier system would translate into semantically similar nouns always sharing the same sets of classifiers. a fully irregular system would translate into semantically similar nouns never sharing the same classifiers. the hypothesis that we are testing here is whether mandarin classifiers do indeed organize mandarin nouns along the two competing communicative pressures related to learning and processing, which have been found to structure gender systems, and strike a balance between regularity and discriminability. using a large set of close to 1m mandarin noun phrases extracted from the leipzig corpora collection @xcite and their corresponding fasttext embeddings @xcite , we show that grammatical noun classification in the sense of @xcite extends beyond one-to-one relations like german noungender pairs, to cases where the nominal classifiers are in a many-to-many relationship with their associated nouns. just like gender markers, nounclassifier combinations are sensitive to noun frequencies, semantic similarity, and possible noun co-occurrence patterns. we find that semantically similar nouns indeed tend to share classifiers if they are infrequent and tend to be distributed across different classifiers when they are frequent. additionally, we find interesting differential patterns between nouns immediately following classifiers and nouns also preceded by one or more nominal modifiers, an interaction that had never been directly investigated and reported before: classifiers will display the expected behavior of a grammatical noun class marker in the sense of @xcite in the scenario where they immediately precede the nouns and are the only contributor to smoothing noun entropy. in cases where additional modifiers (i.e., elements providing additional probabilistic classification) are present in the noun phrase, classifier-noun pairs do not show the structure expected of grammatical noun classification. in such cases and noun classification appears to be most correlated with noun frequencies. our study is the first to quantitatively show the effects of the co-existence of grammatical and probabilistic noun classification. in mandarin, they manifest through differential patterns of class membership depending on both (a) noun pair similarity and (b) noun phrase structure. ## data our study is based on three of the 1m sentence corpora of mandarin chinese in the leipzig corpora collection @xcite . 1 we normalized the data by first transforming all chinese characters into simplified chinese utilizing the open chinese convert software. 2 we then applied the corenlp 3 chinese dependency parser @xcite to our dataset. leveraging the dependency structure, we extracted all complete nominal phrases(1,079,190 instances and 767,721 distinct phrases). since the results of the parsing was not clean enough to conduct a detailed analysis of classifier-noun interactions, we manually filtered the extracted noun phrases as described below. first, we removed nominal phrases with unsual lengths, specifically those exceeding 35 characters -a total of 91 noun phrases. 4 second, any noun phrases that contained elements improperly tagged as classifiers, which included symbols, non-chinese characters, or invalid characters, nps including the same nouns nps including the same classifiers 两张桌子 liǎng zhāng zhuōzi 'two tables' since our goal included evaluating the effect of frequency on noun class membership, we divided our noun data into frequency bands of 30 and sampled 1% from each. we generated nonordered noun pairs for those 1% and computed their summed frequency. from the pairs, we generated a data frame in which the higher and lower frequency elements of pairs were balanced foot_5 across first and second nouns. our resulting data is a set of 390 nouns, sampled from 39,011 nouns across all frequency bands to ensure that the sample reflects distributions in the full data set. from those nouns, we generated 37,831 noun pairs. foot_6 3 method ## discussion and conclusion overall, our results show that noun-classifier combinations are sensitive to the nouns'frequencies and similarities. nouns are overall significantly more likely to share the same classifiers if they are more frequent and/or more semantically similar. co-occurrence likelihood (pmi) mitigates some of those effects. frequency, cosine similarity, and pmi measures capture different communicative scenarios (see description of the interactions between communica-tive pressures in section 1). frequency relates to speakers'familiarity with the data, the likelihood of whether they know the data or need to infer meaning from other data, and how much they rely on the data in communication. cosine similarity as a measure of general semantic similarity corresponds to related meaning speakers can leverage when encountering a rare, previously unknown noun. pmi as a measure of co-occurrence likelihood relates to the likelihood that a speaker would have to differentiate between two nouns in context. we find that for words that tend to co-occur, there is an increase of likelihood of being combined with different classifiers even if two nouns are semantically similar according to their broader distributions. this effect is part of the communicative optimization we observe in the data. while two nouns that co-occur would likely be more semantically similar than two other random nouns, the fact that two nouns never closely co-occur does not mean that they do not otherwise share similarities in their distributions. in a later experiment, we increased the window size for the pmi measures to respectively 3, 5 and 10 word bidirectional windows. the effect was strongest with a 2 or 3-word bidirectional window and completely disappeared with 5 and 10 word windows. this further confirms that if the window is too large, co-occurrence is no longer a factor and cosine similarity and pmi no longer capture two different aspects. what our results illustrate is that languages appear to optimize across frequencies to (i) enhance discriminability to facilitate efficient communication across the most widely used (i.e., most frequent) parts of the system, especially when words are likely to co-occur, and (ii) leverage regularity in the less frequent part of the system to maintain learnability of rarer words and structures potentially encountered late in life.