Jannis Vamvas
PhD Student in
Computational Linguistics
University of Zurich
PhD Student in
Computational Linguistics
University of Zurich
I am a computer scientist specializing in natural language processing (NLP).
Currently, I am studying for a PhD at the Department of Computational Linguistics at University of Zurich, Switzerland, and planning to graduate in Spring 2023. 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 applied science internships at Amazon and 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.
Jannis Vamvas, Johannes Graën and Rico Sennrich. 2023. SwissBERT: The Multilingual Language Model for Switzerland. Pre-print. [bib] [code] [model] [data] [blog]
Jannis Vamvas and Rico Sennrich. 2022. NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 198–213, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. [bib] [code] [blog]
Jannis Vamvas and Rico Sennrich. 2022. As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 490–500, Dublin, Ireland. Association for Computational Linguistics. [bib] [code] [blog]
Renate Hauser, Jannis Vamvas, Sarah Ebling, and Martin Volk. 2022. A Multilingual Simplified Language News Corpus. In Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference, pages 25–30, Marseille, France. European Language Resources Association. [bib] [app]
Jannis Vamvas and Rico Sennrich. 2021. On the Limits of Minimal Pairs in Contrastive Evaluation. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pages 58–68, Punta Cana, Dominican Republic. Association for Computational Linguistics. [bib] [code] [blog] ★ best paper award
Jannis Vamvas and Rico Sennrich. 2021. Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10246–10265, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. [bib] [code] [blog, blog]
Jannis Vamvas and Rico Sennrich. 2020. X-Stance: A Multilingual Multi-Target Dataset for Stance Detection. In Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS), Zurich, Switzerland. [bib] [code] [data] [talk] [blog] ★ best video award
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HS 2022 |
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HS 2021 |
Instructor Ethical Aspects of NLP |
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HS 2021 |
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FS 2021 |
Lecturer Programming Techniques in Computational Linguistics 2 |
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HS 2020 |
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FS 2020 |
Lecturer Programming Techniques in Computational Linguistics 2 |
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HS 2019 |
Large language models can tackle some hard linguistic tasks.
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The Stable Diffusion release inspired me to make tiny concept art.
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Hypothetical reasoning can detect overtranslations and undertranslations.
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Multilingual translation models offer surprising ways of comparing two sentences.
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Forced decisions between sentences are not always predictive of generated language.
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Why not use contrastive sources instead of contrastive translations?
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Introducing a dataset for multilingual and multi-target stance detection.
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A brief essay discussing problems for gender equality in my field of study.
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