Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read
and understand. In order to do so, several rewriting transformations can be performed such …

Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-
sequence framework. However, unlike sequence transduction problems such as machine …

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 …

Recent advancements in high-quality, large-scale English resources have pushed the
frontier of English Automatic Text Simplification (ATS) research. However, less work has …

Split and rephrase is the task of breaking down a sentence into shorter ones that together
convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia's …

Progress in Sentence Simplification has been hindered by the lack of supervised data,
particularly in languages other than English. Previous work has aligned sentences from …

Transformer-based language models (LMs) pretrained on large text collections implicitly
store a wealth of lexical semantic knowledge, but it is non-trivial to extract that knowledge …

Abstract Automatic Readability Assessment aims at assigning a complexity level to a given
text, which could help improve the accessibility to information in specific domains, such as …

Evaluation in machine learning is usually informed by past choices, for example which
datasets or metrics to use. This standardization enables the comparison on equal footing …

In this paper, we present an overview of existing parallel corpora for Automatic Text
Simplification (ATS) in different languages focusing on the approach adopted for their …