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Generative Language Models for Paragraph-Level Question Generation

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.

Asahi Ushio, Fernando Alva-Manchego, Jose Camacho-Collados

Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing

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.

Jeffri Murrugarra-Llerena, Fernando Alva-Manchego, Nils Murrugarra-Llerena

A Benchmark for Neural Readability Assessment of Texts in Spanish

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.

Laura Vásquez-Rodríguez, Pedro-Manuel Cuenca-Jiménez, Sergio Esteban Morales-Esquivel, Fernando Alva-Manchego

Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language

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.

Martina Miliani, Serena Auriemma, Fernando Alva-Manchego, Alessandro Lenci

PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset

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

Gissella Bejarano, Joe Huamani-Malca, Francisco Cerna-Herrera, Fernando Alva-Manchego, Pablo Rivas

Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts

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.

Matthew Shardlow, Fernando Alva-Manchego

Towards Readability-Controlled Machine Translation of COVID-19 Texts

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.

Fernando Alva-Manchego, Matthew Shardlow

deepQuest-py: Large and Distilled Models for Quality Estimation

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

Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia

Validating Quality Estimation in a Computer-Aided Translation Workflow: Speed, Cost and Quality Trade-off

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.

Fernando Alva-Manchego, Lucia Specia, Sara Szoc, Tom Vanallemeersch, Heidi Depraetere

IAPUCP at SemEval-2021 Task 1: Stacking Fine-Tuned Transformers is Almost All You Need for Lexical Complexity Prediction

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

Kervy Rivas Rojas, Fernando Alva-Manchego