About me

Hi, I'm Jannis Vamvas, researcher in Natural Language Processing (NLP) and Academic Associate at University of Zurich.

My research focuses on the application of deep learning to NLP. I am interested in systems that use data in multiple languages and in how their quality can be evaluated.

In Spring 2023, I received a PhD in Computational Linguistics from the University of Zurich. I have been supervised by Rico Sennrich, Lena A. Jäger and Martin Volk.

Previous stages include:

I also have professional experience in full-stack web development and still enjoy dabbling in that area when I have some free time.

Publications

Juri Grosjean and Jannis Vamvas. 2024. Fine-tuning the SwissBERT Encoder Model for Embedding Sentences and Documents. In Proceedings of the 9th edition of the Swiss Text Analytics Conference, Chur, Switzerland. Association for Computational Linguistics. [cite] [code] [model] ★ best scientific paper award

Jannis Vamvas and Rico Sennrich. 2024. Linear-time Minimum Bayes Risk Decoding with Reference Aggregation. Accepted to ACL 2024. [cite] [code]

Jannis Vamvas, Noëmi Aepli and Rico Sennrich. 2024. Modular Adaptation of Multilingual Encoders to Written Swiss German Dialect. In Proceedings of the 1st Workshop on Modular and Open Multilingual NLP (MOOMIN 2024), pages 16–23, St Julians, Malta. Association for Computational Linguistics. [cite] [code] [model] [blog]

Rico Sennrich, Jannis Vamvas and Alireza Mohammadshahi. 2024. Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 21–33, St. Julian’s, Malta. Association for Computational Linguistics. [cite] [code]

Michelle Wastl, Jannis Vamvas and Rico Sennrich. 2023. Machine Translation Models are Zero-Shot Detectors of Translation Direction. Pre-print. [cite] [code] [demo]

Alireza Mohammadshahi, Jannis Vamvas and Rico Sennrich. 2023. Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Models. Accepted to the 2024 Workshop on Insights from Negative Results in NLP. [cite] [code]

Jannis Vamvas and Rico Sennrich. 2023. Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13543–13552, Singapore. Association for Computational Linguistics. [cite] [code] [model] [data] [demo]

Jannis Vamvas, Tobias Domhan, Sony Trenous, Rico Sennrich and Eva Hasler. 2023. Trained MT Metrics Learn to Cope with Machine-translated References. In Proceedings of the Eighth Conference on Machine Translation, pages 983–995, Singapore. Association for Computational Linguistics. [cite] [code]

Jannis Vamvas. 2023. Model-based Evaluation of Multilinguality. Ph.D. thesis, University of Zurich. [cite] [blog]

Jannis Vamvas, Johannes Graën and Rico Sennrich. 2023. SwissBERT: The Multilingual Language Model for Switzerland. In Proceedings of the 8th edition of the Swiss Text Analytics Conference, pages 54–69, Neuchatel, Switzerland. Association for Computational Linguistics. [cite] [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. [cite] [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. [cite] [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. [cite] [data]

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. [cite] [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. [cite] [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. [cite] [code] [data] [talk] [blog] ★ best video award

Recent Posts

An Encoder Model for Swiss German

SwissBERT can now process written Swiss German.

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Wenn ChatGPT den Smartvote-Fragebogen ausfüllt

Sind Sprachmodelle politisch voreingenommen?

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Introducing SwissBERT

The multilingual language model for Switzerland.

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Translation Puzzles are In‑context Learning Tasks

Large language models can tackle some hard linguistic tasks.

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Three Diffusion Digressions

The Stable Diffusion release inspired me to make tiny concept art.

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Lost and Found in Translation

Hypothetical reasoning can detect overtranslations and undertranslations.

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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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