  This paper presents our segmentation system developed for the MLP 2017 shared
tasks on cross-lingual word segmentation and morpheme segmentation. We model
both word and morpheme segmentation as character-level sequence labelling
tasks. The prevalent bidirectional recurrent neural network with conditional
random fields as the output interface is adapted as the baseline system, which
is further improved via ensemble decoding. Our universal system is applied to
and extensively evaluated on all the official data sets without any
language-specific adjustment. The official evaluation results indicate that the
proposed model achieves outstanding accuracies both for word and morpheme
segmentation on all the languages in various types when compared to the other
participating systems.
