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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

Knowledge Distillation for Quality Estimation

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.

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

Controllable Text Simplification with Explicit Paraphrasing

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.

Mounica Maddela, Fernando Alva-Manchego, Wei Xu

ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations

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.

Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoı̂t Sagot, Lucia Specia

EASSE: Easier Automatic Sentence Simplification Evaluation

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

Fernando Alva-Manchego, Louis Martin, Carolina Scarton, Lucia Specia