Kumar, Dhruv2020-07-282020-07-282020-07-282020-07-24http://hdl.handle.net/10012/16084We present a new iterative approach towards unsupervised edit-based sentence simplification. Our approach is guided by a scoring function to select simplified sentences generated after iteratively performing word and phrase-level edits on the complex sentence. The scoring function measures different aspects of simplification: fluency, simplicity, and preservation of meaning. As a result, unlike past approaches, our method is controllable and interpretable and does not require a parallel training set since it is unsupervised. At the same time, using the Newsela and WikiLarge datasets, we experimentally show that our solution is nearly as effective as state-of-the-art supervised approaches.enNatural Language ProcessingMachine LearningText SimplificationMaster Thesis