  The evolution of language follows the rule of gradual change. Grammar,
vocabulary, and lexical semantic shifts take place over time, resulting in a
diachronic linguistic gap. As such, a considerable amount of texts are written
in languages of different eras, which creates obstacles for natural language
processing tasks, such as word segmentation and machine translation. Although
the Chinese language has a long history, previous Chinese natural language
processing research has primarily focused on tasks within a specific era.
Therefore, we propose a cross-era learning framework for Chinese word
segmentation (CWS), CROSSWISE, which uses the Switch-memory (SM) module to
incorporate era-specific linguistic knowledge. Experiments on four corpora from
different eras show that the performance of each corpus significantly improves.
Further analyses also demonstrate that the SM can effectively integrate the
knowledge of the eras into the neural network.
