Jannis Vamvas

About me

I am a PhD student at the Department of Computational Linguistics at the University of Zurich, Switzerland. My main supervisor is Prof. Rico Sennrich.

My research focuses on the application of machine learning to natural language processing. I am interested in systems that use data in multiple languages, and in how their quality can be evaluated.

Having a background in Computer Science and Philosophy, I graduated in 2019 from LMU Munich with an M. Sc. in Computational Linguistics. Previous stages include a research internship at Munich Re and teaching assistance with Prof. Hinrich Schütze at LMU.

I also have professional experience in full-stack web development, which is one of my favourite pastimes.

Publications

Vamvas, Jannis; Sennrich, Rico (2021). On the Limits of Minimal Pairs in Contrastive Evaluation. In: Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, Online and in Punta Cana, Dominican Republic, 11 November 2021 - 11 November 2021.
Vamvas, Jannis; Sennrich, Rico (2021). Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias. In: 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online and in Punta Cana, Dominican Republic, 7 November 2021 - 11 November 2021.
Vamvas, Jannis; Sennrich, Rico (2020). X-Stance: A Multilingual Multi-Target Dataset for Stance Detection. In: 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS), Zurich, 23 June 2020 - 25 June 2020.

Teaching

	HS 2021	Instructor Ethical Aspects of NLP
	HS 2021	Lecturer Language Technology and Web Applications
	FS 2021	Lecturer Programming Techniques in Computational Linguistics 2
	HS 2020	Lecturer Language Technology and Web Applications
	FS 2020	Lecturer Programming Techniques in Computational Linguistics 2
	HS 2019	Tutor Language Technology and Web Applications

About me

Publications

Teaching

Recent Posts

The Limits of Minimal Sentence Pairs

When MT Distillation Leads to Bias

Evaluating Black-Box MT with Contrastive Conditioning

More General Stance Detection with x-Stance

BERT for NER

How NLP affects Gender Equality