Time-Aware Ancient Chinese Text Translation and Inference

TL;DR

This paper introduces a time-aware translation model for ancient Chinese texts that incorporates chronological context to improve translation quality and understanding, validated on a specialized parallel corpus.

Contribution

It proposes a novel multi-label prediction approach that includes era prediction, enhancing translation accuracy by leveraging temporal context.

Findings

Effective translation quality improvement demonstrated

Model successfully predicts both translation and era

Validated on a specialized parallel corpus

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. % As a natural step of generalization, we pivot on the modern Chinese translations to generate multilingual outputs. %We show experimentally the efficacy of our framework in producing quality translation outputs and also validate our framework on a collected task-specific parallel corpus. 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 https://github.com/orina1123/time-aware-ancient-text-translation for future research.