Text simplification (TS) reduces the complexity of the text to improve its readability and
understandability, while possibly retaining its original information content. Over time, TS has …

Since the late 1990s, automatic text simplification (ATS) was promoted as a natural
language processing (NLP) task with great potential to make texts more accessible to …

We present the first attempt at using sequence to sequence neural networks to model text
simplification (TS). Unlike the previously proposed automated TS systems, our neural text …

Progress in sentence simplification has been hindered by a lack of labeled parallel
simplification data, particularly in languages other than English. We introduce MUSS, a …

We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases
when no parallel source-target pairs are available, we train masked language models …

The curse of knowledge can impede communication between experts and laymen. We
propose a new task of expertise style transfer and contribute a manually annotated dataset …

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS
commonly consists of three main steps: complex word identification, substitute generation …

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 …

Edit-based approaches have recently shown promising results on multiple monolingual
sequence transduction tasks. In contrast to conventional sequence-to-sequence (Seq2Seq) …

Even in highly-developed countries, as many as 15–30% of the population can only
understand texts written using a basic vocabulary. Their understanding of everyday texts is …