StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

There are two major classes of natural language grammars -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph. Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependencyconstrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time. Human languages have a rich latent structure. This structure is multifaceted, with the two major classes of grammar being dependency and constituency structures. There have been an exciting breath of recent work that are targeted at learning this structure in a data-driven unsupervised fashion.