ZORA Publikationsliste

Publikationen

• Tang, G., Sennrich, R., & Nivre, J. (2020). Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English 4251–4262. https://www.aclweb.org/anthology/2020.coling-main.375
• Mallinson, J., Sennrich, R., & Lapata, M. (2020). Zero-Shot Crosslingual Sentence Simplification 5109–5126. https://www.aclweb.org/anthology/2020.emnlp-main.415
• Rios, A., Müller, M., & Sennrich, R. (2020). Subword segmentation and a single bridge language affect zero-shot neural machine translation 526–535. http://www.statmt.org/wmt20/pdf/2020.wmt-1.64.pdf
• Zhang, B., Titov, I., & Sennrich, R. (2020). Fast Interleaved Bidirectional Sequence Generation 501–513.
• Amrhein, C., & Sennrich, R. (2020). On Romanization for Model Transfer Between Scripts in Neural Machine Translation 2461–2469. https://www.aclweb.org/anthology/2020.findings-emnlp.223
• Emelin, D., Titov, I., & Sennrich, R. (2020). Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks 7635–7653. https://www.aclweb.org/anthology/2020.emnlp-main.616
• Zhang, B., Titov, I., Haddow, B., & Sennrich, R. (2020). Adaptive Feature Selection for End-to-End Speech Translation 2533–2544. https://www.aclweb.org/anthology/2020.findings-emnlp.230
• Müller, M., Rios, A., & Sennrich, R. (2020). Domain robustness in neural machine translation Proceedings of the 14th Conference of the Association for Machine Translation in the Americas, 151–164. https://www.aclweb.org/anthology/2020.amta-research.14
• Aji, A. F., Bogoychev, N., Heafield, K., & Sennrich, R. (2020). In Neural Machine Translation, What Does Transfer Learning Transfer? 7701–7710. https://www.aclweb.org/anthology/2020.acl-main.688
• Wang, C., & Sennrich, R. (2020). On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation 3544–3552. https://www.aclweb.org/anthology/2020.acl-main.326
• Zhang, B., Williams, P., Titov, I., & Sennrich, R. (2020). Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation 1628–1639. https://www.aclweb.org/anthology/2020.acl-main.148
• Vamvas, J., & Sennrich, R. (2020, June 25). X-Stance: A Multilingual Multi-Target Dataset for Stance Detection 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS), Zurich. http://ceur-ws.org/Vol-2624/paper9.pdf
• Läubli, S., Castilho, S., Neubig, G., Sennrich, R., Shen, Q., & Toral, A. (2020). A Set of Recommendations for Assessing Human–Machine Parity in Language Translation Journal of Artificial Intelligence Research, 67, 653–672. https://doi.org/10.1613/jair.1.11371
• Zhang, B., & Sennrich, R. (2019, December 14). Root Mean Square Layer Normalization Advances in Neural Information Processing Systems 32. Advances in Neural Information Processing Systems 32, Vancouver. http://papers.nips.cc/paper/9403-root-mean-square-layer-normalization.pdf
• Zhang, B., Titov, I., & Sennrich, R. (2019). Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention 897–908. https://www.aclweb.org/anthology/D19-1083.pdf
• Voita, E., Sennrich, R., & Titov, I. (2019). The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives 4387–4397. https://www.aclweb.org/anthology/D19-1448.pdf
• Voita, E., Sennrich, R., & Titov, I. (2019). Context-Aware Monolingual Repair for Neural Machine Translation 876–885. https://www.aclweb.org/anthology/D19-1081.pdf
• Tang, G., Sennrich, R., & Nivre, J. (2019). Encoders Help You Disambiguate Word Senses in Neural Machine Translation 1429–1435. https://doi.org/10.18653/v1/D19-1149
• Wetesko, J., Chochowski, M., Przybysz, P., Williams, P., Grundkiewicz, R., Sennrich, R., Haddow, B., Miceli Barone, A. V., & Birch, A. (2019, November 3). Samsung and University of Edinburgh’s System for the IWSLT 2019 16th International Workshop on Spoken Language Translation 2019, Hong Kong. https://doi.org/10.5281/zenodo.3525537
• Tang, G., Sennrich, R., & Nivre, J. (2019). Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models (G. Angelova, R. Mitkov, I. Nikolova, & I. Temnikova, Eds.; pp. 1186–1193). INCOMA. https://doi.org/10.26615/978-954-452-056-4_136

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