Automatic Reconstruction of A ncient C hinese Pronunciations (2024)

Abstract: Reconstructing ancient Chinese pronunciation is a challenging task due to the scarcity of phonetic records. Different from historical linguistics’ comparative approaches, we reformulate this problem into a temporal prediction task with masked language models, digitizing existing phonology rules into ACP (Ancient Chinese Phonology) dataset of 70,943 entries for 17,001 Chinese characters. Utilizing this dataset and Chinese character glyph information, our transformer-based model demonstrates superior performance on a series of reconstruction tasks, with or without prior phonological knowledge on the target historical period. Our work significantly advances the digitization and computational reconstruction of ancient Chinese phonology, providing a more complete and temporally contextualized resource for computational linguistics and historical research. The dataset and model training code are publicly available.

## introduction a human language is comprised of a pronunciation system and a writing system, both evolving and changing over time. an important part of historical phonology is about reconstructing the historical pronunciation system and its pattern of evolution, sometimes as drastic as the three examples in char t l s y q m 顺 [dýiuin] [ýiu@n] [ciu@n] [cy@n] [ùun] [ùu@n] 席 [zi5k] [zi@k] [sit] [si] [si] [ci] 乏 [biu5p] [viuaep] [fap] [fau] [fa] [fa] historical scripts are often preserved in tangible forms, yet the transmission of pronunciation through successive generations is more susceptible to variation and distortion. across linguistic domains, the systematic evolution of phonetics frequently eludes precise documentation. in the realm of sinological phonology inquiry, even though the characters' glyph information can provide some insight into phonetic attributes, their pronunciations cannot be deterministically inferred from their orthographic form. (see figure 1 ). therefore, unlike phonograms in many other common languages, chinese characters are considered logographic, meaning each phoneme or syllable does not correspond to a specific character. when attempting to reconstruct the chinese phonological system for a certain historical period, only limited resources of pronunciation records are provided. current studies are based on a rhyming dictionary guangyun @xcite published in ad 601. from then on, chinese linguists in different dynasties inherited the taxonomy of character pronunciations in guangyun, and used the comparative method to group similar-sounding characters into the same category. combined with xeno-chinese transliteration 3 , modern dialect pronunciation, etc., the chinese pronunciation system can be partially reconstructed @xcite . given ample attempts and combining different linguistic discoveries, the rule-based reconstruction method has its inherent limitations. first, the pronunciation is reconstructed within each category but not for each character, making the result neither intuitive nor easy to search. second, current reconstruction results cannot cover all chinese characters' pronunciation over all historical periods. chinese language system's evolution is not strictly under the taxonomy of guangyun (see sec. 2), making it impossible to always apply rulebased change patterns on a whole category 4 . third, current results are not fully digitized but only in printed versions. as a matter of fact, the process of character-wise pronunciation reconstruction is complex, encompassing the entirety of chinese characters across various historical eras, and necessitates prior knowledge in linguistics, thereby calling for further refinement. recently, several computer-assisted methods have been proposed to reconstruct ancient chinese pronunciation, including @xcite 's comparative chinese dialect dataset and kim et al. (2023)'s approach of applying transformer model on this dataset to rebuild ancient chinese pronunciation. however, the reconstruction results are limited to a specific historical period, i.e., middle-tang chinese pronunciation. hence, we attempt to build a time-aware dataset and use a temporal factor-embedded model to complete the reconstruction at an arbitrary time point. the contribution of this paper is as follows: ## acp dataset construction our acp (ancient chinese pronunciation) dataset offers character-wise chronological data of ancient chinese pronunciation, combining two kinds of data: the digitized guangyun data (kanjidatabase-project, 2004) and the phonological reconstruction result of @xcite . for a given character, the former informs us the category it belongs to, and the latter tells us the pronunciation reconstruction results on each category. guangyun is a rhyme dictionary using a special sound annotation called fanqie. chinese characters are monosyllabic, i.e. all chinese character's pronunciation correspond to one syllable, each comprised of an initial and a final @xcite . according to fanqie, each chinese character's pronunciation is described as a combination of two representative characters, one for its initial and another for its final as shown in figure 2 . figure 2: method of fanqie: "东" belongs to category "德"([t]) for initial and to category "红"([un])for final. under this taxonomy, there are 38 categories of initials and 298 categories of finals. the aim of phonological reconstruction is to attach the exact pronunciation (denoted by ipa phoneme) onto each initial and final category for targeted period. these information can be found in @xcite 's reconstruction results of chinese phonology, which includes the evolution of chinese pronunciation system from preqin (-206 bc) to modern chinese (ad 1912-) at 9 different representative time points. we have only selected results for 6 historical periods from middletang dynasty and beyond: middletang, latetang, song, yuan, mingqing and modern, represented by the beginning each historical period (ad 581, ad 836, ad 960, ad 1279, ad 1368, ad 1912), since these reconstruction results are more consistent among different linguists @xcite . see figure 3 for the chinese history timeline. when constructing ancient chinese pronunciation for each character in acp dataset, 4 possible cases are presented. concrete examples for different cases of reconstruction can be found in direct determination of pronunciation if the exact pronunciation is directly given for one category, then the given ipa phoneme is attached onto each character within this category. for example, "波" belongs to initial category of "帮" and [p] is attached to category "帮", then the initial ipa of "波" is @xcite . this is the case for pronunciation system of middletang since all characters strictly belongs to its category denoted in guangyun. rule-based determination of pronunciation if there exists several possible pronunciations for the same category, then the linguistic rules given by wang are applied to help choose the correct one. rules on initials' pronunciation are usually based on finals' category, and rules on finals' pronunciation are usually based on the articulation information recorded in guangyun foot_0 . for example, "砩"="帮"+"废" and "碑"="帮"+"支" both belongs to initial category of "帮", but given the linguistic rule that "帮" represents [f] only under the case when final category is 废, and represents [p] in other cases, we attach [f] as 砩's initial ipa and [p] as 碑's initial ipa. this is the case for late-tang and song period since a small amount of categories' pronunciation has encountered rule-based change. ## pronunciation reconstruction model in this section, we introduce the architecture of our pronunciation reconstruction model, centered around figure 5 . by utilizing an embedding layer and a transformer encoder layer, our model is capable of learning complex patterns and relationships within both the phonetic data and the glyph information, enabling accurate reconstructions across different historical periods. ## evaluations in this section, we comprehensively evaluate the performance of gtenhanced transformer across various evaluation tasks in our dataset. ## conclusion we introduce an extensive ancient chinese pronunciation dataset with 70,943 entries for 17,001 chinese characters, alongside an enhanced transformer-based model integrating glyph and temporal information to refine traditional phonological reconstruction results. our model outperforms traditional methods across various ancient chinese pronunciation reconstruction tasks with superior accuracy even under low-resource scenarios. despite the incomplete phonetic data, it maintains high performance for reconstructing and predicting chinese pronunciations. we offer a richer, temporally contextualized resource for computational linguistics and historical research. this study lays a strong foundation for future research in phonetic reconstruction and language evolution.