  Discourse segmentation aims to segment Elementary Discourse Units (EDUs) and
is a fundamental task in discourse analysis. For Chinese, previous researches
identify EDUs just through discriminating the functions of punctuations. In
this paper, we argue that Chinese EDUs may not end at the punctuation positions
and should follow the definition of EDU in RST-DT. With this definition, we
conduct Chinese discourse segmentation with the help of English labeled
data.Using discourse commonality between English and Chinese, we design an
adversarial neural network framework to extract common language-independent
features and language-specific features which are useful for discourse
segmentation, when there is no or only a small scale of Chinese labeled data
available. Experiments on discourse segmentation demonstrate that our models
can leverage common features from bilingual data, and learn efficient
Chinese-specific features from a small amount of Chinese labeled data,
outperforming the baseline models.
