Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Table 1: Full WSJ (test set) unsupervised unlabeled binary constituency parsing including punctuation. † indicates trained to optimize NLI task. Mean and max are calculated over five random restarts. PRPN F1 was calculated using the parse trees and results provided by Htut et al. (2018). The depth (δ) is the average tree height. +PP refers to post-processing heuristic that attaches trailing punctuation to the root of the tree. The top F1 value in each column is bolded.
['[BOLD] Model', 'F1 [ITALIC] μ', 'F1 [ITALIC] max', '[ITALIC] δ']
This model achieves a mean F1 7 points higher than ON-LSTM and an increase of over 6.5 max F1 points. We also see that DIORA exhibits much less variance between random seeds than ON-LSTM. Additionally, we find that PRPN-UP and DIORA benefit much more from the +PP heuristic than PRPN-LM. This is consistent with qualitative analysis showing that DIORA and PRPN-UP incorrectly attach trailing punctuation much more often than PRPN-LM.