I am a Lecturer (~Assistant Professor) at the School of Computer Science and Informatics at Cardiff University. My research focuses on technologies that apply Artificial Intelligence for information accessibility. In particular, my work employs Natural Language Processing approaches to facilitate reading and understanding. I am especially interested in studying the real capabilities of systems for several Natural Language Generation tasks, such as Text Simplification, Summarisation and Machine Translation. In order to do that, my collaborators and I create language resources, design evaluation methodologies or metrics, and implement models using machine learning techniques.
Previously, I was a Research Associate at SheffieldNLP (2020-2021), working with Prof. Lucia Specia for the APE-QUEST and Bergamot projects on Quality Estimation for Machine Translation. Before that, I worked as Adjunct Professor at the Pontifical Catholic University of Peru (2013-2016), where I was a member of the Artificial Intelligence Group IA-PUCP. During my Masters, I was also a member of the Interinstitutional Center for Computational Linguistics at the University of São Paulo.
Looking for PhD Students! I am interested in supervising PhD students in projects involving Natural Language Processing for Text Adaptation. Please, check the relevant pages in FindAPhD for more information, depending on whether you are a self-funded student, or plan on applying to a School scholarhip. Do not hesitate to contact me if you have any questions!
PhD in Computer Science, 2021
University of Sheffield
MSc in Computer Science, 2013
University of Sao Paulo
BSc in Informatics Engineering, 2009
Pontifical Catholic University of Peru
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 investigate how well existing metrics can assess sentence-level simplifications where multiple operations may have been applied and which, therefore, require more general simplicity judgements.
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation