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 this https URL

我们提出了针对古代汉语的《尔雅》翻译方法，该方法通过从各种来源收集、清理和分类古代汉语材料，形成迄今为止最广泛的古代汉语资源。我们设计了两个同时运作的任务：双音节对齐替代（DAS）和双层掩蔽语言模型（DMLM）。在不同场景下，我们建立了基准来评判古代汉语翻译质量，并评估了各种现有模型的古代汉语翻译能力。我们的模型在五个领域展现了卓越的零 - shot 性能，与 GPT-3.5 模型相比具有 + 12.0 BLEU 的得分，并且在人工评估结果上优于 ERNIE Bot。随后的微调进一步展示了《尔雅》模型的卓越转移能力，获得了 + 6.2 BLEU 的提升。我们在此链接上发布了上述所有资源。