Time-Aware A ncient C hinese Text Translation and Inference (2021)

Abstract: In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data for future research.

## introduction the chinese language inherits a lot of phrases from ancient time @xcite and is spoken by roughly 1.3 billion native speakers. however, the language's ancient variant (or ancient chinese) is mastered by a few and proved to be a bottleneck in understanding the essence of the chinese culture. building a translation system from the ancient chinese to the modern text thus serves a few important purposes: (i) the ancient chinese is considered as an essential part of the curriculum in all of the chinese-speaking regions foot_1 , so an ancient chinese translation system can be used to bolster the immediate understanding of ancient texts. (ii) further, the translation system can help to settle the linguistic debate with regard to the era of origin of an independent segment of text. this is especially useful for the identification of a discovered artifacts where carbon dating cannot pinpoint the exact era, but where their linguistic features can formulate a clear-cut dynasty or time period. however, it is not without challenge in constructing such translation systems. one primary obstacle lies in the extensive timeline where ancient texts can be derived -one segment of ancient text can come from the pre-qin (先秦) era, and another coming from the song dynasty (宋朝), which are roughly about 700 years apart. this gap witnessed a drastic evolution of linguistic properties where the usage of phrases became imbued with different meanings. besides, different eras often consist of various amounts of available data, and thus the same translation model training will be exposed to data imbalance, which complicates the design of the translation systems and limits their generalizability. on the other hand, past attempts at building such translation systems yield poor performance that renders them practically unusable as-is in the practical settings @xcite -these efforts are still largely limited as parallel data is scarce for some eras. recent advances in machine translation and text style transfer/generation utilize semi-supervised techniques to tackle similar challenges by aligning latent representations from different styles for the low resource scenarios @xcite @xcite @xcite @xcite . to this end, we aim to bridge this gap that makes the following contributions: ## background at a fine-grained view, the notion of "ancient chinese" may not be considered a single language with a static word-meaning mapping. therefore, we direct our efforts toward three particular eras: pre-qin (先秦), han (汉), and song (宋) to verify the hypothesis that the chronology of a text directly influences the word meaning and model performance. in particular, pre-qin and han are closer chronologically, so we expect their model performances to be closer than that between pre-qin and song, as was shown in other ancient text translation @xcite . one reason for this difference is the use of polysemous single-character words, which are highly ambiguous. some words begin to lose meanings over time. for example, in ancient chinese, the word 看('kàn') has many meanings such as "to visit" and "to listen", in addition to the major modern meaning, "to look". as the language evolved, vocabulary changed and lexical semantic shift took place, creating diachronic semantic gaps that may introduce subtle differences in the understanding of the text. for instance, the earliest known meaning of "看" is "to look into the distance". the meaning of "to look at something closely" emerged during the han period and eventually became the prominent meaning of this verb in modern chinese. in sum, the language change across time suggests a modeling approach that is aware of when the text was written. ## task formulation we assume two nonparallel datasets a and m of sentences in ancient chinese (zh-a) and modern chinese (zh-m) respectively.a parallel dataset p that contains the pairs of sentences in both variants of text is also present. the sizes of the three datasets are denoted as |a|, |m | and |p |, respectively. as the nonparallel data is abundant but the parallel data is limited, size |a|, |m | |p |. the main objective is to convert the input ancient chinese text a to its modern variant m. this task is akin to style transfer, or if the text are drastically different, machine translation. in this paper, we are only concerned with the direction from zh-a to zh-m. additionally, we include the prediction of the chronological period of the ancient text as an auxiliary task. ## proposed framework our framework translates the given ancient chinese text ( §4.1) while providing additional chronological context information ( §4.2) (see ## dataset construction we obtain parallel ancient-modern chinese sentence pairs, and nonparallel ancient (zh-a) and modern chinese (zh-m) sentences from two sources @xcite . ## experimental settings we tokenized both ancient and modern chinese text by splitting characters. the vocabulary sizes are 4,824 and 4,600 respectively. we built our model upon the fairseq toolkit foot_2 . the architecture is transformer with about 54m parameters, which largely follows the configuration of @xcite . translations were generated with beam size 5, and we consider top 5 candidates for reranking. for the multi-label prediction model, we adapted existing code foot_3 to build a gpt-2 language model reranker with approximately 82m parameters. first, we pre-trained the model with 1.2 gb of chinese wikipedia text. then, we fine-tuned the pre-trained model with the chronologicallyannotated training data. for each ancient-modern sentence pair with chronology information, we ## main results overall, we observe from ## conclusion in this paper, we present a framework that translates ancient chinese texts into its modern correspondence in low resource scenarios with very little parallel data and a larger set of nonparallel sentences without ancient-modern alignment information. we display the importance and usefulness of chronology inference as an auxiliary task that hints at potential diachronic semantic gaps. we hope to extend this research to further model additional contextual information about each era.