Unsupervised Neural Text Simplification
Sai Surya, Abhijit K. Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan · 2019
The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.The core framework is composed of a shared encoder and a pair of attentional-decoders, crucially assisted by discrimination-based losses and denoising.The framework is trained using unlabeled text collected from en-Wikipedia dump.Our analysis (both quantitative and qualitative involving human evaluators) on public test data shows that the proposed model can perform text-simplification at both lexical and syntactic levels, competitive to existing supervised methods.It also outperforms viable unsupervised baselines.Adding a few labeled pairs helps improve the performance further.