Rule Augmented Unsupervised Constituency Parsing
Unsupervised parsing of syntactic trees has gained considerable attention. Aprototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders. However, no mechanism ensures that the learnt model leverages the well-understood language grammar. We propose an approach that utilizes very generic lingustic knowledge of the language present in the form of syntactic rules, thus inducing better syntactic structures. We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
The work will appear in the Findings of ACL 2021.