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We introduce QG-Bench, a multilingual and multidomain benchmark for question generation, which we use to evaluate the performance of robust baselines based on fine-tuning generative language models, as well as to assess the reliability of automatic metrics commonly-used used for the task.

We propose an approach for obtaining representations of courses in a curriculum based on a novel course-guided attention mechanism and metric learning, and test it in a new dataset with curricula of computing programs from the USA and LATAM.

We compile a new benchmark for automated readability assessments of texts in Spanish, and fine-tune pre-trained language models to perform the task at both sentence and paragraph levels.

We introduce Admin-It, a new dataset for sentence-level readability assessment of Italian administrative texts, and evaluate the performance of Neural Pairwise Ranking models in this new data.

We present a framework for creating a multi-modal Peruvian sign language interpretation dataset based on videos.

We introduce Simple TICO-19, a new language resource containing manual simplifications of the English and Spanish portions of the TICO-19 corpus for Machine Translation of COVID-19 literature.

This project proposes to investigate the capabilities of machine translation models for generating translations at varying levels of readability, focusing on texts about COVID-19.

We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation

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 …