Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Table 2: Directed dependency accuracy on section 23 of WSJ, evaluating on sentences of length ⩽10 and all lengths. Starred entries (∗) denote that the system benefits from additional punctuation-based constraints. Standard deviation is given in parentheses when available.
['[BOLD] System', '⩽10', 'all']
Since our approach induces latent POS tags jointly with dependency trees, it may be able to learn POS clusters that are more amenable to grammar induction than the unsupervised tags. We observe that CRFAE underperforms its gold-tag counterpart substantially. This may largely be a result of the model’s reliance on prior linguistic rules that become unavailable when gold POS tag types are unknown. Many extensions to DMV can be considered orthogonal to our approach – they essentially focus on improving the syntax model. It is possible that incorporating these more sophisticated syntax models into our approach may lead to further improvements.