Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
Table 6: F1 for different model variants on the binary WSJ validation set with included punctuation. The binary trees are as-is (∅) or modified according to the post-processing heuristic (+PP). The mean F1 is shown across three random seeds.
['[BOLD] Composition', '[BOLD] Loss', 'F1 [ITALIC] μ ∅', 'F1 [ITALIC] μ +PP']
We see that MLP composition consistently performs better than with TreeLSTM, that MLP benefits from the Softmax loss, and that the best performance comes from sharing parameters. All other experimental results use this highly performant setting unless otherwise specified.