Multi-Dialectal Representation Learning of Sinitic Phonology (2023)

Abstract: Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages systems. Motivated by this, this paper provides an approach for obtaining multi-dialectal representations of Sinitic syllables, by constructing a knowledge graph from structured phonological data ,then applying the BoxE technique from knowledge base learning. We applied unsupervised clustering techniques to the obtained representations to observe that the representations capture phonemic contrast from the input dialects. Furthermore, we trained classifiers to perform inference of unobserved Middle Chinese labels, showing the representations’ potential for indicating archaic, proto-language features. The representations can be used for performing completion of fragmented Sinitic phonological knowledge bases, estimating divergences between different characters, or aiding the exploration and reconstruction of archaic features.

## introduction the evolution of languages in the sinitic family created intricate correspondences and divergences in its dense dialect clusters. investigating the dynamics of this evolution, through comparison and proto-language reconstruction, is an essential task to sinitic historical phonology. however, it may be costly for researchers to manually probe through the groups in search of phonological hints. hence, it is desirable to accelerate the process with modern algorithms, specifically, representation learning. graph-based machine learning @xcite have gained increasing attention in recent years, due to their versatility with data with flexible structures. especially, missing link prediction algorithms for knowledge graphs @xcite ) @xcite can uncover a latent structure in noisy and incomplete knowledge. in the case for learning phonological representations, using graphbased learning can allow for more comprehensive integration of multi-dialectal evidence. thus, we propose applying graph-based techniques for multidialectal representation learning. we construct a knowledge graph from the multidialectal phonological data, by abstracting unique phonetic components and individual characters into two kinds of nodes. then, we connect them with edges specific to the dialect type wherein the character is associated with the given component. on the constructed knowledge graph, we train the boxe algorithm @xcite , a box embedding model for knowledge base completion. finally, we evaluate the obtained representations with unsupervised and supervised clustering, as well as mlp probes based on middle-chinese-derived labels, to show this tool's value for sinitic phonological investigation. ## background on sinitic languages the analysis of sinitic languages face a few specific challenges due to unique phonological characteristics. these characteristics define crucial details of our design. in sinitic languages, morphemes are primarily monosyllabic. hence, chinese writing binds one syllable to each of its glyphs, known as characters. a syllable in sinitic can be decomposed into an initial, a final and a tone. @xcite initials refer to the consonant-like sounds at the beginning of a syllable, which include both stops (e.g. /p-/, /b-/) and fricatives (e.g. /s-/, /s-/). these initials could be combined with various finals to form syllables. finals refer to the vowel-like sounds at the end of a syllable, which included both simple vowels (e.g. /a/, /-i/, /-u/), complex vowels (e.g. /-ai/, /-ao/, /-ei/), and vowels combined with consonant codas (/-m/,/n/,/-n/,/-p/,/-t/,/-k/). tones refer to the pitch patterns associated with syllables in chinese. tones could distinguish between words that were otherwise homophonous, and they were an important part of the chinese phonological system. due to the early conception of the chinese writing system, syllables from different sinitic languages can usually be aligned to each other through a written form. as this alignment is typically implemented in databases of raw sinitic data, the difficulty of cognate identification is drastically reduced, facilitating analysis. however, the simple syllable structure introduces large amounts of homophones, words sharing same pronunciations, into sinitic languages. this hinders the use of the comparative method in reconstructing a sinitic proto-language. the existence of a supersegmental tone feature also complicates a historical analysis of sinitic languages. two factors that motivate the use of a graphbased method include the uniform structure of sinitic syllables and their intimate relationship with characters. the intuitive syllable decomposition and the glyph-based alignment inspire viewing the components contextualized in various dialects as different "observations" of a single character. theoretically, these observations are derivable from the reading of the character in the proto-language. ## related work the practice of computationally-aided protolanguage construction, often associated with cognate identification, has been extensively considered in the past two decades @xcite . examples include @xcite which draws insights from bio-informatics and the classical comparative workflow, and @xcite , which compared many methods for cognate identification. an relevant insight from the latter paper is that language-specific methods often outperform language-general ones, especially for languages like sinitic. an epitome of neural methods for proto-language reconstruction would be @xcite , in which latin is reconstructed from romance descendent languages with a encoderdecoder structure. though, our approach differs from their study in many crucial aspects. in meloni et al. 2021, the reconstruction is supervised, with the proto-language latin provided at training time. but our method targets not only documented proto-languages like middle chinese, but also unknown, intermediate varieties in the development from ancient sinitic to modern dialects, which requires an unsupervised approach. additionally, in term of techniques, their use of gru and attentionbased transducers contrasts with our emphasis on a graph-based method. considering the representation learning of sinitic, we found abundant literature on the topic of speech recognition @xcite , segmentation and synthesis, which often yield representations of certain phonological relevance as by-product. though, these studies devote heavily to a few major languages, specifically mandarin or cantonese, and, since they are rarely claim motivation from historical phonology, seldom take a multi-lingual or multi-dialectal approach. while speech representation learning often serve the aformentioned purposes, the proposals of using neural networks to model phonetics and phonology from either symbolic abstractions or acoustic data in order to examine theories in these fields are relevant to this study. unsupervised binary stochastic autoencoders were explored in @xcite . gan (generative adversarial networks) was used in @xcite . these proposals modeled perception and categorization, in relation to language acquisation. most interestingly, representation learning has been applied for discovering phonemic tone contours in tonal languages @xcite , of which a great portion are sinitic languages. however, these proposals again rarely address issues from historical phonology. finally, it should be noted that the concept of transforming porous data in a regular, matrix-like form to a loose, graph-like form for flexibility in processing, while essential to the designs of this paper, is not novel in the literature. rather, it orig-inates with the grape framework in @xcite . notably, when the data in question concerns chinese historical phonology, it coincides with johann-mattis list's proposals for introducing network methods into computational linguistics and chinese historical phonology. generally, this line of work should be considered most relevant to our study @xcite @xcite . @xcite approaches issues spanning character formation, middle chinese annotation, as well as old chinese reconstruction with network methods. list et al. (2014); list (2015) examines dialect evolution with display graphs, with a focus on the complex word-borrowing dynamics between the dialect families. he calls for colleagues to lend more attention to data-driven, quantitative methods. our proposal answers list's call by bringing together knowledge graphs with chinese historical phonology. furthermore, the utilization of sota representation learning extends beyond the scope of the aforementioned work. ## method the graph-based method for representing dialect data has the benefit of making the model more flexible, robust, and efficient at using porous, incomplete data. this is particularly important since investigations into dialects are often uncoordinated, resulting in a large amount of partial character entries, where only some columns have pronunciations while others are missing. it could be argued that we can use missing data imputation to alleviate the issue, and continue processing the dialect data in a matrix form, perhaps with feed-forward neural networks or denoising autoencoders @xcite . however, traditional missing-data imputation techniques may create fictitious syllables that violate the phonotactics of that dialect when imputing initials or finals according to the mode of a type. conditioning the initials or finals on each other will cause higher-order dependencies that are hard to solve. therefore, by keeping the spaces untouched and using paired comparisons, the graph formalism circumnavigates the problem. this formulation may also allow for auxiliary input features, such as basic phonological knowledge about the nature of phonemic contrast, to be injected into the model. on this graph, we learn the embeddings with the boxe algorithm, to be discussed below. we expressed the data with a knowledge graph and trained the representations through an auxiliary task of completing the multi-dialectal knowledge graph. with a graph-based technique, the representations can be more robust to noisy and porous data. additionally, the method will be more flexible, allowing for auxiliary input features to be injected. ## data and experimental setup we use pronunciation data from four varieties of xiang chinese changsha 長沙, shuangfeng 雙峰, guanyang wenshi 灌陽文市, and quanzhou xiancheng 全州縣城., spoken primarily in hunan province, provided by ccr @xcite , and retrived with comparative analysis toolset for chinese dialects @xcite . we also obtain labels of middle chinese readings from the same source. in this work, middle chinese refers to the phonological system recorded in the dictionary qieyun, from the year 601 ad. it was supplemented in the song dynasty into the dictionary guangyun, from which this study draws data. middle chinese is literary and may not reflect the colloquial speech of china in any time or place. however, most phonological systems of modern sinitic languages (with the notable exception of the min languages) can be derived from the qieyun system. thus we treat it as a useful protolanguge model for most sinitic languages. we operate on symbolic abstractions instead of raw acoustic data, as all the data have been transcribed into ipa in the database. one row of data corresponds to readings of one chinese character. internally, each character is mapped to a unique identifier, which is the character's serial number in guangyun. for every variety of chinese, there are four columns, corresponding to initial value, final value, tonal value and tonal type of a given character's pronuciation. the tone type argument is actually redundant, and it is assigned manually by investigators. in each dialect, there is a one-toone correspondence between one tone value with one tone type. between two dialects, tones arising from the same middle chinese tone are given same names. hence, the tone type feature introduces prior expert knowledge about the historical origin of tones. however, we expect the model to derive the historical tones without any diachronic expert knowledge. hence, we discard the tone type feature, and use only the three values for this study. ## discussions our current setting only operates on pre-abstracted symbols and lacks incorporation of acoustic or articulatory evidence. incorporating multi-modal data into a knowledge graph framework could enhance the quality of embeddings and enable more accurate representations of phonological features. alsp, the proposed method uses shared embeddings for symbolic components across different dialects, which cannot fully capture dialect-specific variations. investigating contextualized or dialectspecific component embeddings could improve the model's ability to capture finer-grained phonological distinctions. finally, phonetically similar components are currently treated as independent items, which is too absolute an assumption. however, it is also possible for phonetic cues to override the correct phonological alignment in the model. in many cases, phonetic similarity does not imply diachronic homology. two phonetically equivalent syllables from two different dialects may have different origins. conversely, two phonetically distinct syllables from two different dialects may be cognate. the subtle balance between "phonetic" and "phonological" proximity requires further discussion. several lines of research may benefit from robust multi-dialectal representations. in dialectology, there is need for estimating divergence between phonological systems. that includes the divergences between its constituents, such as individual characters, phonemes and syllables. with multi-dialectal representations, this divergence can be estimated quantitatively. in historical phonology, the reconstruction of a proto-language demands deep scrutiny of dialect systems whose efficiency can be improved with manipulating the representations. also, they can be used for completion of the phonological knowledge base. often knowledge bases for sinitic phonology are fragmented, due to imperfect surveys and heterogeneity of sources, etc. the representations can be used to infer missing pronunciations in different dialects to improve the quality of observations. the graph-based method proposed in this paper benefits from phonological characteristics specific to sinitic languages, but is also limited by these characteristics. specifically, the process of constructing a phonological graph from words, as proposed in this study, is less natural in languages where words typically have many syllables, and vary in the number of syllables contained. in these languages, the temporal interaction of syllables within a word is a new phenomena that the graphbased method needs to adapt to. additionally, in these languages, it will be less straightforward to tokenize the words into expressive sub-words to use as nodes in the graph. presumably, in non-sinitic languages, the proposed method will be most performant in other languages of the southeast asian sprachbund, such as those in the hmong-mien or austroasiatic families. these languages share phonological features with sinitic languages that enable our method. on the other hand, this method will likely meet more complications outside of the local sprachbund. ## conclusion this paper demonstrated the potential of graphbased representation learning in chinese historical phonology. the representations are potent in many ways, i.e. facilitating the reconstruction of minor proto-languages. in the future, more sophisticated techniques such as deep learning models could be explored to further improve the quality of the obtained representations. furthermore, the proposed method can be integrated with other linguistic resources, such as recordings, articulatory time series, or orthographic corpora, to enrich the knowledge base and improve the accuracy of reconstructions. with the development of modern, massive linguistic datasets such as nk2028(nk2028, 2020), cognet @xcite or morphynet @xcite as well as improvements in large pre-trained models, we can expect foundational models that possess emergent and meta-generalizing capabilities to arise in historical phonology or morphology. this avenue of research holds great promise for advancing our understanding of the phonology and evolution of sinitic languages, and potentially other language families as well.