Antonio Toral

Professor · Neural Machine Translation

Zurich University of Applied Sciences (ZHAW)

Switzerland

About

Antonio Toral is a leading researcher at the University of Groningen, focusing on Neural Machine Translation (NMT), human evaluation, and parallel corpus curation. His work spans low-resource language modeling, lexical diversity enhancement, and multilingual figurative language detection.

Affiliations: University of Groningen, MaCoCu Project, CREAMT Consortium
Key projects:
- MaCoCu (Massive collection of under-resourced language data)
- CREAMT (Creativity in literary translation)

Research interests include:

Improving NMT naturalness and lexical richness
Document-level evaluation of machine translations
Character-level modeling and downsampling techniques
Reproducibility challenges in human NLP evaluation
Cross-lingual formality transfer without parallel data

His recent articles (2021–2025) demonstrate expertise in:

Reinforcement learning for naturalness preservation
Statistical analysis of translationese effects
Dependency-based reordering models
Pivot translation for Catalan→Chinese
Domain-specific corpus creation for EU Digital Service Infrastructures

Research fields

Neural Machine Translation Computational Linguistics Natural Language Processing Low-Resource Language Modeling Human Evaluation of NLP Systems Lexical Diversity in Translation Multilingual Corpora Curation Character-Level and Subword Modeling Formality Style Transfer

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