Rule Augmented Unsupervised Constituency Parsing

• URL: http://arxiv.org/abs/2105.10193v1
• Date: Fri, 21 May 2021 08:06:11 GMT
• Title: Rule Augmented Unsupervised Constituency Parsing
• Authors: Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan and Rishabh Iyer
• Abstract summary: We propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic rules. We achieve new state-of-the-art results on two benchmarks datasets, MNLI and WSJ.
• Score: 11.775897250472116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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