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
this https URL for future
research.

本文旨在解决翻译古代中文文本所面临的语言困境和信息缺失，提出采用多标签预测任务，预测翻译和具体年代，并以现代中文翻译为基础扩展为多语言输出，实验证明该框架在产生高质量翻译输出方面的有效性。