Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Table 3: WSJ-10 and WSJ-40 unsupervised non-binary unlabeled constituency parsing with punctuation removed. † indicates that the model predicts a full, non-binary parse with additional resources. ‡ indicates model was trained on WSJ data and PRPNNLI was trained on MultiNLI data. CCM uses predicted POS tags while CCMgold uses gold POS tags. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). LB and RB are the left and right-branching baselines. UB is the upper bound attainable by a model that produces binary trees.
['[BOLD] Model', '[BOLD] WSJ-10 F1 [ITALIC] μ', '[BOLD] WSJ-10 F1 [ITALIC] max', '[BOLD] WSJ-40 F1 [ITALIC] μ', '[BOLD] WSJ-40 F1 [ITALIC] max']
We also compare our models to two subsets of the WSJ dataset that were used in previous unsupervised parsing evaluations. WSJ-10 and WSJ-40 contain sentences up to length 10 and 40 respectively after punctuation removal. The WSJ-10 split has been difficult for latent tree parsers such as DIORA, PRPN, and ON-LSTM, none of which (including our model) are able to improve upon previous non-neural methods. However, when we compare trends between WSJ-10 and WSJ-40, we see that DIORA does a better job at extending to longer sequences.