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
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.
He, He, et al.. (2021). IRM---when it works and when it doesn't: A test case of natural language inference. Neural Information Processing Systems. 34.5 indexed citations
10.
Rosenfeld, Amir, et al.. (2020). A Constructive Prediction of the Generalization Error Across Scales. International Conference on Learning Representations.7 indexed citations
11.
Vig, Jesse, Sebastian Gehrmann, Yonatan Belinkov, et al.. (2020). Investigating Gender Bias in Language Models Using Causal Mediation Analysis. Neural Information Processing Systems. 33. 12388–12401.84 indexed citations
Belinkov, Yonatan & Yonatan Bisk. (2018). Synthetic and Natural Noise Both Break Neural Machine Translation. International Conference on Learning Representations.144 indexed citations
14.
Dalvi, Fahim, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, & Stephan Vogel. (2017). Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder. International Joint Conference on Natural Language Processing. 1. 142–151.26 indexed citations
15.
Belinkov, Yonatan & James Glass. (2017). Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems. Neural Information Processing Systems. 30. 2441–2451.18 indexed citations
16.
Belinkov, Yonatan, Lluı́s Màrquez, Hassan Sajjad, et al.. (2017). Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks. International Joint Conference on Natural Language Processing. 1. 1–10.39 indexed citations
17.
Romeo, Salvatore, Giovanni Da San Martino, Alberto Barrón‐Cedeño, et al.. (2016). Neural Attention for Learning to Rank Questions in Community Question Answering. International Conference on Computational Linguistics. 1734–1745.22 indexed citations
18.
Belinkov, Yonatan, et al.. (2016). Shamela: A Large-Scale Historical Arabic Corpus. International Conference on Computational Linguistics. 45–53.1 indexed citations
19.
Belinkov, Yonatan, et al.. (2014). arTenTen: Arabic Corpus and Word Sketches. Journal of King Saud University - Computer and Information Sciences. 26(4). 357–371.24 indexed citations
20.
Sajjad, Hassan, Kareem Darwish, & Yonatan Belinkov. (2013). Translating Dialectal Arabic to English. Meeting of the Association for Computational Linguistics. 1–6.31 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.