I am a second year PhD student with the UK Research & Innovation Centre for Doctoral Training in Natural Language Processing at the University of Edinburgh. Previously, I graduated from the master in AI from the University of Amsterdam.

I am interested in conducting AI research by taking human intelligence as a point of reference and adapting computational models such as to reflect human inductive biases. The facet of human intelligence that intrigues me most is natural language. Within the field of NLP my main research interest is the evaluation of and improvement of techniques for compositional generalisation in neural models of language. Previously, I worked on multitask learning for metaphor detection.

Selected Publications

  • Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation
    Verna Dankers, Christopher Lucas, Ivan Titov
    In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3608-3626 [paper]
  • The Paradox of the Compositionality of Natural Language: A Neural Machine Translation Case Study
    Verna Dankers, Elia Bruni, Dieuwke Hupkes
    In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4154-4175 [paper]
  • Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network
    Verna Dankers*, Anna Langedijk*, Kate McCurdy, Adina Williams, Dieuwke Hupkes (*equal contribution)
    In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 94-108 [paper]
    🏅Best paper award.