Yonatan Belinkov

56 papers receiving 1.0k citations

Peers

Yonatan Belinkov
Comparison fields: 5 of 79
  • Artificial Intelligence 1.0k
  • Computer Vision and Pattern Recognition 262
  • Information Systems 120
  • Signal Processing 60
  • Sociology and Political Science 28
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Countries citing papers authored by Yonatan Belinkov

Since Specialization
Citations

This map shows the geographic impact of Yonatan Belinkov'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 Yonatan Belinkov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yonatan Belinkov more than expected).

Fields of papers citing papers by Yonatan Belinkov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yonatan Belinkov. 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 Yonatan Belinkov. The network helps show where Yonatan Belinkov may publish in the future.

Co-authorship network of co-authors of Yonatan Belinkov

This figure shows the co-authorship network connecting the top 25 collaborators of Yonatan Belinkov. A scholar is included among the top collaborators of Yonatan Belinkov 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 Yonatan Belinkov. Yonatan Belinkov 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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7 1
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IRM---when it works and when it doesn't: A test case of natural language inference
5
9 8
10 66
11
A Constructive Prediction of the Generalization Error Across Scales
7
12
Investigating Gender Bias in Language Models Using Causal Mediation Analysis
84
13
Synthetic and Natural Noise Both Break Neural Machine Translation
144
14
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder
26
15
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
18
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Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks
39
17
Shamela: A Large-Scale Historical Arabic Corpus
1
18
Neural Attention for Learning to Rank Questions in Community Question Answering
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Translating Dialectal Arabic to English
31

About Yonatan Belinkov

Yonatan Belinkov is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and General Social Sciences, having authored 62 papers that have together received 1.1k indexed citations. Recurring topics across this work include Topic Modeling (37 papers), Natural Language Processing Techniques (33 papers) and Multimodal Machine Learning Applications (7 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Health Informatics (19 citations) and Computer Vision and Pattern Recognition (262 citations). Yonatan Belinkov has collaborated with scholars based in Israel, United States and Qatar. Frequent co-authors include James Glass, Yonatan Bisk, Hassan Sajjad, James Henderson, Rabeeh Karimi Mahabadi, Sebastian Gehrmann, Nadir Durrani, Fahim Dalvi, Kareem Darwish and Daniel Nevo. Their work appears in journals such as Bioinformatics, IBM Journal of Research and Development and Information Processing & Management.

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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