Time-Aware Ancient Chinese Text Translation and Inference
This work addresses the problem of poor translation quality for ancient Chinese texts, which is incremental as it builds on past techniques by incorporating chronological context.
The paper tackles the challenge of translating ancient Chinese text by addressing linguistic gaps and missing contextual information, proposing a multi-label prediction task that predicts translation and era to improve quality, and demonstrates efficacy on a parallel corpus with chronology annotations.
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.