We introduce deep inside-outside recursive autoencoders (DIORA), a
fully-unsupervised method for discovering syntax that simultaneously learns
representations for constituents within the induced tree. Our approach predicts
each word in an input sentence conditioned on the rest of the sentence and uses
inside-outside dynamic programming to consider all possible binary trees over
the sentence. At test time the CKY algorithm extracts the highest scoring
parse. DIORA achieves a new state-of-the-art F1 in unsupervised binary
constituency parsing (unlabeled) in two benchmark datasets, WSJ and MultiNLI.