The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

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 …

In order to simplify a sentence, human editors perform multiple rewriting transformations:
they split it into several shorter sentences, paraphrase words (ie replacing complex words or …

Machine translation systems based on deep neural networks are expensive to train.
Curriculum learning aims to address this issue by choosing the order in which samples are …

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

Short texts (STs) present in a variety of scenarios, including query, dialog, and entity names.
Most of the exciting studies in neural machine translation (NMT) are focused on tackling …

This work introduces a machine translation task where the output is aimed at audiences of
different levels of target language proficiency. We collect a high quality dataset of news …

Text encoding is one of the most important steps in Natural Language Processing (NLP). It
has been done well by the self-attention mechanism in the current state-of-the-art …

Automatic text simplification (TS) aims to automate the process of rewriting text to make it
easier for people to read. A pre-requisite for TS to be useful is that it should convey …

With the increase of translation demand, the advancement of information technology, the
development of linguistic theories and the progress of natural language understanding …