Research blog

My research focuses on deep learning for natural language processing (NLP). I am interested in systems that use data in multiple languages and in how their quality can be evaluated.

Highlights from my research:

• PhD thesis on Model-Based Evaluation of Multilinguality: Summary, Published thesis
• SwissBERT, the multilingual language model for Switzerland: Explainer, Model weights

Questions that intrigue me:

• How can maximum-quality text be generated with language models?
• How can large language models be adapted to local data?
• Since language models estimate word probabilities – what are creative ways of using those probabilities? Ideas we developed previously include: Contrastive Conditioning, Translation Cross-Likelihood, Omission Error Detection and Translation Direction Detection

• Since January 2024: Academic associate at the Department of Computational Linguistics
• April 2023 − December 2023: Postdoctoral researcher, MUTAMUR project
• 2019 − March 2023: PhD student at the Department of Computational Linguistics, supervised by Rico Sennrich, Lena A. Jäger and Martin Volk.
• Summer 2022: Applied Science Internship with Amazon AI Translate, Berlin
• 2018−2019: Research internship at Munich Re (NLP for Reinsurance Development)
• 2018−2019: Graduate teaching assistant for Prof. Dr. Hinrich Schütze, CIS Munich
• 2017−2019: M.Sc. in Computational Linguistics (major) and Computer Science (minor) at LMU Munich
• 2015−2017: Full-Stack Web Developer at Arteria GmbH, Basel
• 2011−2015: B.A. in Computer Science and Philosophy from the University of Basel

Jannis Vamvas and Rico Sennrich. 2024. Linear-time Minimum Bayes Risk Decoding with Reference Aggregation. Pre-print. [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. Pre-print. [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] [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. [cite] [code] [data] [talk] [blog] ★ best video award