Antonio Toral

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