Fernando Alva-Manchego
Scholar

Fernando Alva-Manchego

Google Scholar ID: 4SnHu7sAAAAJ
Cardiff University
Text SimplificationReadability AssessmentText AdaptationEducational NLPNatural Language Processing
Citations & Impact
All-time
Citations
1,347
 
H-index
16
 
i10-index
21
 
Publications
20
 
Co-authors
55
list available
Resume (English only)
Academic Achievements
  • Published multiple papers covering areas such as readability assessment and text simplification. Examples include: 'UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment' (arXiv, 2025); 'Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation' (NAACL 2025); 'Analysing Zero-Shot Readability-Controlled Sentence Simplification' (COLING 2025); 'The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline' (BEA 2024); 'BLESS: Benchmarking Large Language Models on Sentence Simplification' (EMNLP 2023); 'An Empirical Comparison of LM-based Question and Answer Generation Methods' (Findings of ACL 2023).
Research Experience
  • Currently a Lecturer at the School of Computer Science and Informatics, Cardiff University. Previously, a Research Associate at SheffieldNLP (2020-2021), working with Prof. Lucia Specia on Quality Estimation for Machine Translation projects APE-QUEST and Bergamot. Also worked as an Adjunct Professor at the Pontifical Catholic University of Peru (2013-2016), where he was a member of the Artificial Intelligence Group IA-PUCP. During his Masters, he was also a member of the Interinstitutional Center for Computational Linguistics at the University of São Paulo.
Education
  • PhD in Computer Science, University of Sheffield; MSc in Computer Science, University of Sao Paulo; BSc in Informatics Engineering, Pontifical Catholic University of Peru.
Background
  • Research interests include Text-to-Text Generation (e.g. Text Simplification, Summarisation, Machine Translation, etc.), Evaluation of Natural Language Generation, Writing Assistance, and Natural Language Processing for Education. Focuses on technologies that apply Artificial Intelligence for education and information accessibility.
Miscellany
  • Has given several academic talks on topics such as automatic evaluation of sentence simplification, challenges in evaluating simplified texts, and multi-operation automatic text simplification.