Rico Sennrich

104 papers receiving 2.8k citations

Hit Papers

Analyzing Multi-Head Self-Attention: Specialized Heads Do...20192026202120232019100200300400500

Peers

Rico Sennrich
Comparison fields: 5 of 121
  • Artificial Intelligence 2.7k
  • Computer Vision and Pattern Recognition 944
  • Information Systems 224
  • Molecular Biology 131
  • Language and Linguistics 121
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Qun Liu China
Kenneth Heafield United Kingdom
Hai Zhao China
Sergey Edunov United States
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Mike Lewis United States
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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

20 of 20 papers shown
#WorkIndexed citations
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6 25
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11 74
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Improving Machine Translation of Educational Content via Crowdsourcing
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13 179
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Syntax-aware Neural Machine Translation Using CCG.
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Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers
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Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015
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Proceedings of the Ninth Workshop on Statistical Machine Translation
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Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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20 41

About Rico Sennrich

Rico Sennrich is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Language and Linguistics, having authored 109 papers that have together received 3.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (96 papers), Topic Modeling (83 papers) and Text Readability and Simplification (26 papers). The work is most often cited by research in Artificial Intelligence (2.7k citations), Computer Vision and Pattern Recognition (944 citations) and Language and Linguistics (121 citations). Rico Sennrich has collaborated with scholars based in Switzerland, United Kingdom and Netherlands. Frequent co-authors include Ivan Titov, Elena Voita, David Talbot, Fédor Moiseev, Barry Haddow, Martin Volk, Alexandra Birch, Samuel Läubli, Gongbo Tang and Antonio Valerio Miceli Barone. Their work appears in journals such as Computational Linguistics, Computer Speech & Language and Transactions of the Association for Computational Linguistics.

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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