Rico Sennrich

19.1k total citations · 1 hit paper
109 papers, 3.1k citations indexed

About

Rico Sennrich is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Language and Linguistics. According to data from OpenAlex, Rico Sennrich has authored 109 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 101 papers in Artificial Intelligence, 17 papers in Computer Vision and Pattern Recognition and 7 papers in Language and Linguistics. Recurrent topics in Rico Sennrich's work include Natural Language Processing Techniques (96 papers), Topic Modeling (83 papers) and Text Readability and Simplification (26 papers). Rico Sennrich is often cited by papers focused on Natural Language Processing Techniques (96 papers), Topic Modeling (83 papers) and Text Readability and Simplification (26 papers). Rico Sennrich collaborates with scholars based in Switzerland, United Kingdom and Netherlands. Rico Sennrich's co-authors include Ivan Titov, Elena Voita, David Talbot, Fédor Moiseev, Barry Haddow, Martin Volk, Alexandra Birch, Gongbo Tang, Samuel Läubli and Antonio Valerio Miceli Barone and has published in prestigious journals such as Computational Linguistics, Computer Speech & Language and Transactions of the Association for Computational Linguistics.

In The Last Decade

Rico Sennrich

104 papers receiving 2.8k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

2019 568 citations Elena Voita, Rico Sennrich et al. UvA-DARE (University of Amsterdam) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Rico Sennrich Switzerland	27	2.7k	944	224	131	121	109	3.1k
Kenneth Heafield United Kingdom	22	2.5k 1.0×	550 0.6×	311 1.4×	162 1.2×	69 0.6×	56	2.8k
Marco Turchi Italy	26	2.4k 0.9×	372 0.4×	260 1.2×	126 1.0×	107 0.9×	136	2.8k
Sivaji Bandyopadhyay India	26	2.8k 1.0×	479 0.5×	368 1.6×	86 0.7×	98 0.8×	291	3.4k
Hai Zhao China	31	2.7k 1.0×	708 0.8×	273 1.2×	220 1.7×	38 0.3×	218	3.2k
Kevin Duh United States	24	2.1k 0.8×	490 0.5×	216 1.0×	101 0.8×	62 0.5×	148	2.4k
Min Zhang China	30	2.4k 0.9×	992 1.1×	300 1.3×	168 1.3×	30 0.2×	234	3.2k
Holger Schwenk France	26	3.5k 1.3×	816 0.9×	264 1.2×	142 1.1×	57 0.5×	93	3.8k
Chengqing Zong China	30	3.4k 1.3×	894 0.9×	388 1.7×	149 1.1×	45 0.4×	249	3.9k
Eiichiro Sumita Japan	32	3.8k 1.4×	940 1.0×	184 0.8×	203 1.5×	132 1.1×	333	4.0k
Jason Baldridge United States	30	2.2k 0.8×	842 0.9×	269 1.2×	84 0.6×	76 0.6×	89	3.0k

Countries citing papers authored by Rico Sennrich

Since Specialization

Citations

This map shows the geographic impact of Rico Sennrich's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rico Sennrich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rico Sennrich more than expected).

Fields of papers citing papers by Rico Sennrich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rico Sennrich. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rico Sennrich. The network helps show where Rico Sennrich may publish in the future.

Co-authorship network of co-authors of Rico Sennrich

This figure shows the co-authorship network connecting the top 25 collaborators of Rico Sennrich. A scholar is included among the top collaborators of Rico Sennrich based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rico Sennrich. Rico Sennrich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Clematide, Simon, et al.. (2025). Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples. 2161–2170. 1 indexed citations

Sennrich, Rico, et al.. (2024). An Analysis of BPE Vocabulary Trimming in Neural Machine Translation. 48–50. 1 indexed citations

Sennrich, Rico, et al.. (2024). Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding. 21–33. 2 indexed citations

Moryossef, Amit, et al.. (2024). SignCLIP: Connecting Text and Sign Language by Contrastive Learning. 9171–9193. 2 indexed citations

Sennrich, Rico, et al.. (2023). Improving the Cross-Lingual Generalisation in Visual Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13419–13427. 4 indexed citations

Sennrich, Rico, et al.. (2023). A Benchmark for Evaluating Machine Translation Metrics on Dialects without Standard Orthography. 1045–1065.

Sen, Sukanta, Rico Sennrich, Biao Zhang, & Barry Haddow. (2023). Self-training Reduces Flicker in Retranslation-based Simultaneous Translation. Zurich Open Repository and Archive (University of Zurich). 3734–3744. 1 indexed citations

Sennrich, Rico, et al.. (2022). Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET. Zurich Open Repository and Archive (University of Zurich). 1125–1141. 7 indexed citations

Zhang, Biao, et al.. (2021). Sparse Attention with Linear Units. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 6507–6520. 19 indexed citations

10.

Sennrich, Rico, et al.. (2021). How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?. Zurich Open Repository and Archive (University of Zurich). 689–705. 7 indexed citations

11.

Emelin, Denis & Rico Sennrich. (2021). Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8517–8532. 8 indexed citations

12.

Voita, Elena, Rico Sennrich, & Ivan Titov. (2019). The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives. UvA-DARE (University of Amsterdam). 4395–4405. 74 indexed citations

13.

Tang, Gongbo, et al.. (2018). Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures. Edinburgh Research Explorer (University of Edinburgh). 4263–4272. 179 indexed citations

14.

Barone, Antonio Valerio Miceli, Rico Sennrich, Menno van Zaanen, et al.. (2018). Improving Machine Translation of Educational Content via Crowdsourcing. Arrow@dit (Dublin Institute of Technology). 2 indexed citations

15.

Nădejde, Maria, Siva Reddy, Rico Sennrich, et al.. (2017). Syntax-aware Neural Machine Translation Using CCG.. arXiv (Cornell University). 9 indexed citations

16.

Williams, Philip, Rico Sennrich, Maria Nădejde, et al.. (2016). Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers. 4 indexed citations

17.

Williams, Philip, Rico Sennrich, Maria Nădejde, Matthias Huck, & Philipp Koehn. (2015). Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015. 3 indexed citations

18.

Sennrich, Rico. (2012). Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. 162 indexed citations

19.

Sennrich, Rico. (2011). Combining multi-engine machine translation and online learning through dynamic phrase tables. Zurich Open Repository and Archive (University of Zurich). 2 indexed citations

20.

Sennrich, Rico & Martin Volk. (2010). MT-based sentence alignment for OCR-generated parallel texts. Zurich Open Repository and Archive (University of Zurich). 41 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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