  Multi-criteria Chinese word segmentation is a promising but challenging task,
which exploits several different segmentation criteria and mines their common
underlying knowledge. In this paper, we propose a flexible multi-criteria
learning for Chinese word segmentation. Usually, a segmentation criterion could
be decomposed into multiple sub-criteria, which are shareable with other
segmentation criteria. The process of word segmentation is a routing among
these sub-criteria. From this perspective, we present Switch-LSTMs to segment
words, which consist of several long short-term memory neural networks (LSTM),
and a switcher to automatically switch the routing among these LSTMs. With
these auto-switched LSTMs, our model provides a more flexible solution for
multi-criteria CWS, which is also easy to transfer the learned knowledge to new
criteria. Experiments show that our model obtains significant improvements on
eight corpora with heterogeneous segmentation criteria, compared to the
previous method and single-criterion learning.
