  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.
