Toward Fast and Accurate Neural Discourse Segmentation
Yi‐Zhong Wang, Sujian Li, Jingfeng Yang · 2018
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 endto-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 stateof-the-art performance.1