ZORA Publikationsliste

Publikationen

• Vamvas, J., Aepli, N., & Sennrich, R. (2024). Modular Adaptation of Multilingual Encoders to Written Swiss German Dialect 16–23. https://aclanthology.org/2024.moomin-1.3
• Vamvas, J., & Sennrich, R. (2023). Towards Unsupervised Recognition of Token-level Semantic Differences in Related Documents Proceedings of the Conference on Empirical Methods in Natural Language Processing, 13543–13552. https://doi.org/10.18653/v1/2023.emnlp-main.835
• Aepli, N., Amrhein, C., Schottmann, F., & Sennrich, R. (2023). A Benchmark for Evaluating Machine Translation Metrics on Dialects without Standard Orthography In P. Koehn, B. Haddow, T. Kocmi, & C. Monz (Eds.), Proceedings of the Eighth Conference on Machine Translation (pp. 1045–1065). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.wmt-1.99
• Vamvas, J., Domhan, T., Trenous, S., Sennrich, R., & Hasler, E. (2023). Trained MT Metrics Learn to Cope with Machine-translated References Proceedings of Conference on Machine Translation, 983–995. https://doi.org/10.18653/v1/2023.wmt-1.95
• Kew, T., & Sennrich, R. (2023). Uncovering Hidden Consequences of Pre-training Objectives in Sequence-to-Sequence Models 7010–7022. https://doi.org/10.18653/v1/2023.findings-acl.438
• Amrhein, C., Schottmann, F., Sennrich, R., & Läubli, S. (2023). Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model 4486–4506. https://doi.org/10.18653/v1/2023.acl-long.246
• Tedeschi, S., Bos, J., Declerck, T., Hajič, J., Hershcovich, D., Hovy, E., Koller, A., Krek, S., Schockaert, S., Sennrich, R., Shutova, E., & Navigli, R. (2023). What’s the Meaning of Superhuman Performance in Today’s NLU? 12471–12491. https://doi.org/10.18653/v1/2023.acl-long.697
• Vamvas, J., Graën, J., & Sennrich, R. (2023). SwissBERT: The Multilingual Language Model for Switzerland Proceedings of the Swiss Text Analytics Conference, 54–69. https://aclanthology.org/2023.swisstext-1.6.pdf
• Winata, G. I., Aji, A. F., Cahyawijaya, S., Mahendra, R., Koto, F., Romadhony, A., Kurniawan, K., Moeljadi, D., Prasojo, R. E., Fung, P., Baldwin, T., Lau, J. H., Sennrich, R., & Ruder, S. (2023). NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages 815–834. https://doi.org/10.18653/v1/2023.eacl-main.57
• Zhang, B., Haddow, B., & Sennrich, R. (2023). Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation 2264–2276. https://doi.org/10.18653/v1/2023.eacl-main.166
• Sen, S., Sennrich, R., Zhang, B., & Haddow, B. (2023). Self-training Reduces Flicker in Retranslation-based Simultaneous Translation 3734–3744. https://doi.org/10.18653/v1/2023.eacl-main.270
• Nooralahzadeh, F., & Sennrich, R. (2023). Improving the Cross-Lingual Generalisation in Visual Question Answering (No. 02982; ArXiv.Org). The Thirty-Seventh AAAI Conference on Artificial Intelligence. https://doi.org/10.48550/arXiv.2209.02982
• Armitage, J., Impett, L., & Sennrich, R. (2023). A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues Proceedings of the IEEE Workshop on Applications of Computer Vision, online. https://doi.org/10.1109/WACV56688.2023.00115
• Nickl, R., Stöcklin, D. U., Glock, H. J., Bubenhofer, N., & Sennrich, R. (2023). Interview : Wir und ChatGPT «Wie ein Schweizer Taschenmesser». UZH Magazin: die Zeitschrift der Universität Zürich : die Wissenschaftszeitschrift, 60–65. https://www.news.uzh.ch/dam/jcr:2fc9d6d4-160c-4138-ae7f-d737f956c889/UZH_Magazin_2_23.pdf
• Nooralahzadeh, F., & Sennrich, R. (2023). Improving the Cross-Lingual Generalisation in Visual Question Answering Proceedings of the AAAI Conference on Artificial Intelligence, 37, 13419–13427. https://doi.org/10.1609/aaai.v37i11.26574
• Zhang, B., Müller, M., & Sennrich, R. (2023). SLTUNET: A Simple Unified Model for Sign Language Translation The Eleventh International Conference on Learning Representations, Kigali Rwanda. https://openreview.net/forum?id=EBS4C77p_5S
• Vamvas, J., & Sennrich, R. (2022). NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures (No. 2022). 198–213. https://perma.cc/4KW7-7BNN
• Amrhein, C., & Sennrich, R. (2022, November). Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Online. https://doi.org/10.48550/arXiv.2202.05148
• Jiang, Y. E., Liu, T., Ma, S., Zhang, D., Yang, J., Huang, H., Sennrich, R., Cotterell, R., Sachan, M., & Zhou, M. (2022). BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation 1550–1565. https://aclanthology.org/2022.naacl-main.111
• Aepli, N., & Sennrich, R. (2022). Improving Zero-Shot Cross-lingual Transfer Between Closely Related Languages by Injecting Character-Level Noise 4074–4083. https://aclanthology.org/2022.findings-acl.321

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