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.
Natural Questions: A Benchmark for Question Answering Research
2019984 citationsTom Kwiatkowski, Jennimaria Palomaki et al.Transactions of the Association for Computational Linguisticsprofile →
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 Andrew M. Dai'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 Andrew M. Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew M. Dai more than expected).
This network shows the impact of papers produced by Andrew M. Dai. 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 Andrew M. Dai. The network helps show where Andrew M. Dai may publish in the future.
Co-authorship network of co-authors of Andrew M. Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew M. Dai.
A scholar is included among the top collaborators of Andrew M. Dai 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 Andrew M. Dai. Andrew M. Dai is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rajkomar, Alvin, et al.. (2019). Improved Patient Classification with Language Model Pretraining Over Clinical Notes.. arXiv (Cornell University).2 indexed citations
3.
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 →
4.
Chen, Mia Xu, Gagan Bansal, Yuan Cao, et al.. (2019). Gmail Smart Compose. 2287–2295.77 indexed citations
5.
Choi, Edward, Zhen Xu, Yujia Li, et al.. (2019). Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records..9 indexed citations
6.
Huang, Cheng-Zhi Anna, Ashish Vaswani, Jakob Uszkoreit, et al.. (2018). An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. arXiv (Cornell University).21 indexed citations
7.
Dai, Andrew M. & Quoc V. Le. (2015). Semi-supervised Sequence Learning. arXiv (Cornell University). 28. 3079–3087.201 indexed citations
8.
Macherey, Klaus, Andrew M. Dai, David Talbot, Ashok C. Popat, & Franz Josef Och. (2011). Language-independent compound splitting with morphological operations. Meeting of the Association for Computational Linguistics. 1395–1404.33 indexed citations
9.
Dai, Andrew M. & Amos Storkey. (2009). Author Disambiguation: A Nonparametric Topic andCo-authorship Model.5 indexed citations
10.
Dai, Andrew M. & Amos Storkey. (2009). Proceedings of NIPS Workshop on Applications for Topic Models Text and Beyond.21 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.