Sentence Simplification in context of Automatic Question Generation

dc.contributor.author	Yarish, Dzvenymyra
dc.date.accessioned	2024-02-14T16:24:25Z
dc.date.available	2024-02-14T16:24:25Z
dc.date.issued	2019
dc.identifier.citation	Yarish, Dzvenymyra. Sentence Simplification in context of Automatic Question Generation / Yarish, Dzvenymyra; Supervisor: Jan Sedivy; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2019. – 36 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4483
dc.language.iso	en	uk
dc.title	Sentence Simplification in context of Automatic Question Generation	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Automatic question generation is a promising field of research. Sentence simplifi- cation is a key to the high-quality question generation. In this work we explore the existing sentence simplification approaches, present an valuable extension to the ex- isting sentence simplification datasets and experiment with the latest seq2seq tech- niques. Our model makes use of transfer learning, which to our knowledge, is the first attempt to apply that method to sentence simplification. We evaluate our model against the rule - based approach and a baseline machine learning model and see im- provements in the paraphrasing component of sentence simplification.	uk