Two-pass Discourse Segmentation with Pairing and Global Features
Table 2: Characteristics of the training and the test set in RST-DT.
['[EMPTY]', '[BOLD] Training', '[BOLD] Test']
We first study how our proposed two-pass discourse segmenter based on pairing features performs against existing segmentation models. In this experiment, we train our linear-chain CRF models on the RST Discourse Treebank (RST-DT) By convention, the corpus is partitioned into a training set of 347 documents and a test set of 38 documents.