  Recently, unsupervised parsing of syntactic trees has gained considerable
attention. A prototypical 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 linguistic 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. We achieve new
state-of-the-art results on two benchmarks datasets, MNLI and WSJ. The source
code of the paper is available at https://github.com/anshuln/Diora_with_rules.
