  Interpreting ancient Chinese has been the key to comprehending vast Chinese
literature, tradition, and civilization. In this paper, we propose Erya for
ancient Chinese translation. From a dataset perspective, we collect, clean, and
classify ancient Chinese materials from various sources, forming the most
extensive ancient Chinese resource to date. From a model perspective, we devise
Erya training method oriented towards ancient Chinese. We design two
jointly-working tasks: disyllabic aligned substitution (DAS) and dual masked
language model (DMLM). From an evaluation perspective, we build a benchmark to
judge ancient Chinese translation quality in different scenarios and evaluate
the ancient Chinese translation capacities of various existing models. Our
model exhibits remarkable zero-shot performance across five domains, with over
+12.0 BLEU against GPT-3.5 models and better human evaluation results than
ERNIE Bot. Subsequent fine-tuning further shows the superior transfer
capability of Erya model with +6.2 BLEU gain. We release all the
above-mentioned resources at https://github.com/RUCAIBox/Erya.
