  We introduce a generic seq2seq parsing framework that casts constituency
parsing problems (syntactic and discourse parsing) into a series of conditional
splitting decisions. Our parsing model estimates the conditional probability
distribution of possible splitting points in a given text span and supports
efficient top-down decoding, which is linear in number of nodes. The
conditional splitting formulation together with efficient beam search inference
facilitate structural consistency without relying on expensive structured
inference. Crucially, for discourse analysis we show that in our formulation,
discourse segmentation can be framed as a special case of parsing which allows
us to perform discourse parsing without requiring segmentation as a
pre-requisite. Experiments show that our model achieves good results on the
standard syntactic parsing tasks under settings with/without pre-trained
representations and rivals state-of-the-art (SoTA) methods that are more
computationally expensive than ours. In discourse parsing, our method
outperforms SoTA by a good margin.
