Improving a Strong Neural Parser with Conjunction-Specific Features
Table 3: Parsing scores on the PTB test-set (Stanford Dependencies).
['System', 'UAS', 'LAS']
The extended parser achieves 0.1 points improvement in UAS and 0.2 points in LAS comparing to Kiperwasser and Goldberg \shortcitekiperwasser2016simple. This is a strong baseline, which so far held the highest results among greedy transition based parsers that were trained on the PTB only, including e.g. the parsers of Weiss et al \shortciteweiss2015structured, Dyer et al \shortcitedyer2015transition and Ballesteros et al \shortciteballesteros2016training. Stronger absolute parsing numbers are reported by Andor et al \shortciteandor2016globally (using a beam); and Kuncoro et al \shortcitekuncoro2016distilling and Dozat and Manning \shortcitedozat2016deep (using an arc-factored global parsers). All those parsers rely on broadly the same kind of features, and while we did not test this, it is likely the conjunction features would benefit them as well.