Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
Table 1: ListOps results: tagging accuracy (Acc.) and attachment score for the latent tree grammar (Att.).
['[EMPTY]', '[BOLD] Acc.', '[BOLD] Att.']
We draw two conclusions from these results. First, using the ST estimator hurts performance, even though we do not relax at test time. Second, the MC approximations, unlike the non-stochastic model, produces latent structures almost identical to gold trees. The non-stochastic version is however relatively successful in terms of tagging accuracy: we hypothesize that the LSTM model solved the problem and uses trees as messages to communicate solutions. See extra analysis in App.