  Chinese word segmentation has entered the deep learning era which greatly
reduces the hassle of feature engineering. Recently, some researchers attempted
to treat it as character-level translation, which further simplified model
designing, but there is a performance gap between the translation-based
approach and other methods. This motivates our work, in which we apply the best
practices from low-resource neural machine translation to supervised Chinese
segmentation. We examine a series of techniques including regularization, data
augmentation, objective weighting, transfer learning, and ensembling. Compared
to previous works, our low-resource translation-based method maintains the
effortless model design, yet achieves the same result as state of the art in
the constrained evaluation without using additional data.
