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

Jannis Vamvas and Rico Sennrich. 2022. NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures . arXiv preprint arXiv:2204.13692 [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]

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] [blogblog]

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

Recent Posts

NMTScore: Text Similarity via Translation

Multilingual translation models offer surprising ways of comparing two sentences.

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The Limits of Minimal Sentence Pairs

Forced decisions between sentences are not always predictive of generated language.

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When MT Distillation Leads to Bias

Distilled translation models tend to overgeneralize.

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Evaluating Black-Box MT with Contrastive Conditioning

Why not use contrastive sources instead of contrastive translations?

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More General Stance Detection with x-Stance

Introducing a dataset for multilingual and multi-target stance detection.

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BERT for NER

How to apply BERT to the task of named entity recognition.

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How NLP affects Gender Equality

A brief essay discussing problems for gender equality in my field of study.

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