The OPT submission to the Shared Task \nof the 2016 Conference on Natural Language \nLearning (CoNLL) implements a \n‘classic’ pipeline architecture, combining \nbinary classification of (candidate) explicit \nconnectives, heuristic rules for non-explicit \ndiscourse relations, ranking and ‘editing’ \nof syntactic constituents for argument identification, \nand an ensemble of classifiers to \nassign discourse senses. With an end-toend \nperformance of 27.77 F1 on the English \n‘blind’ test data, our system advances \nthe previous state of the art (Wang & Lan, \n2015) by close to four F1 points, with particularly \ngood results for the argument identification \nsub-tasks. OPT system results appear \nmore competitive on the new, ‘blind’ \ntest data than on the ‘test’ and ‘development’ \nsections of the Penn Discourse Treebank \n(PDTB; Prasad et al., 2008), which \nmay indicate reduced over-fitting to specific \nproperties of the venerableWall Street \nJournal (WSJ) text underlying the PDTB