  Discourse segmentation, which segments texts into Elementary Discourse Units,
is a fundamental step in discourse analysis. Previous discourse segmenters rely
on complicated hand-crafted features and are not practical in actual use. In
this paper, we propose an end-to-end neural segmenter based on BiLSTM-CRF
framework. To improve its accuracy, we address the problem of data
insufficiency by transferring a word representation model that is trained on a
large corpus. We also propose a restricted self-attention mechanism in order to
capture useful information within a neighborhood. Experiments on the RST-DT
corpus show that our model is significantly faster than previous methods, while
achieving new state-of-the-art performance.
