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.
201914.5k citationsJacob Devlin, Ming‐Wei Chang et al.profile →
Natural Questions: A Benchmark for Question Answering Research
2019984 citationsTom Kwiatkowski, Jennimaria Palomaki et al.Transactions of the Association for Computational Linguisticsprofile →
Fast and Robust Neural Network Joint Models for Statistical Machine Translation
2014293 citationsJacob Devlin, Rabih Zbib et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jacob Devlin'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 Jacob Devlin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Devlin more than expected).
This network shows the impact of papers produced by Jacob Devlin. 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 Jacob Devlin. The network helps show where Jacob Devlin may publish in the future.
Co-authorship network of co-authors of Jacob Devlin
This figure shows the co-authorship network connecting the top 25 collaborators of Jacob Devlin.
A scholar is included among the top collaborators of Jacob Devlin 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 Jacob Devlin. Jacob Devlin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kwiatkowski, Tom, Jennimaria Palomaki, Michael Collins, et al.. (2019). Natural Questions: A Benchmark for Question Answering Research. Transactions of the Association for Computational Linguistics. 7. 453–466.984 indexed citations breakdown →
Huang, Ting-Hao, Francis Ferraro, Nasrin Mostafazadeh, et al.. (2016). Visual Storytelling. 1233–1239.138 indexed citations
8.
Ferraro, Francis, Nasrin Mostafazadeh, Ting-Hao Huang, et al.. (2015). On Available Corpora for Empirical Methods in Vision & Language.. arXiv (Cornell University).1 indexed citations
Devlin, Jacob & Spyros Matsoukas. (2012). Trait-Based Hypothesis Selection For Machine Translation. North American Chapter of the Association for Computational Linguistics. 528–532.9 indexed citations
14.
Zbib, Rabih, Jacob Devlin, David Stallard, et al.. (2012). Machine Translation of Arabic Dialects. North American Chapter of the Association for Computational Linguistics. 49–59.121 indexed citations
15.
Stallard, David, et al.. (2012). Unsupervised Morphology Rivals Supervised Morphology for Arabic MT. Meeting of the Association for Computational Linguistics. 322–327.12 indexed citations
Devlin, Jacob, Antti-Veikko Rosti, Sankaranarayanan Ananthakrishnan, & Spyros Matsoukas. (2011). System Combination Using Discriminative Cross-Adaptation. International Joint Conference on Natural Language Processing. 667–675.2 indexed citations
18.
Huang, Zhongqiang, Jacob Devlin, & Spyros Matsoukas. (2011). BBN's Systems for the Chinese-English Sub-task of the NTCIR-10 PatentMT Evaluation.. NTCIR.8 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.