1

We set up a case-study on the trade-off between speed, cost and quality, investigating the benefits of Quality Estimation models in a real-world scenario, where we rely on end-user acceptability as quality metric.

This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages …

We propose to transfer knowledge from a strong Quality Estimation (QE) teacher model to a much smaller model with a different, shallower architecture, leading to light-weight QE models that perform competitively with distilled pre-trained representations.

We propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting style.

We introduce ASSET, a dataset for assessing sentence simplification in English. ASSET is a crowdsourced multi-reference corpus where each simplification was produced by executing several rewriting transformations.

We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification systems.

Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations …

Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a …

Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel …

We introduce MASSAlign: a Python library for the alignment and annotation of monolingual comparable documents. MASSAlign offers …