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

Toward Fast and Accurate Neural Discourse Segmentation