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.
This map shows the geographic impact of David Chiang'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 David Chiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Chiang more than expected).
This network shows the impact of papers produced by David Chiang. 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 David Chiang. The network helps show where David Chiang may publish in the future.
Co-authorship network of co-authors of David Chiang
This figure shows the co-authorship network connecting the top 25 collaborators of David Chiang.
A scholar is included among the top collaborators of David Chiang 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 David Chiang. David Chiang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Riloff, Ellen, et al.. (2018). Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Empirical Methods in Natural Language Processing.6 indexed citations
7.
Nguyen, Toan & David Chiang. (2017). Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation. International Joint Conference on Natural Language Processing. 2. 296–301.49 indexed citations
8.
Chiang, David, et al.. (2013). Parsing Graphs with Hyperedge Replacement Grammars. Oxford University Research Archive (ORA) (University of Oxford). 924–932.34 indexed citations
Chiang, David. (2012). Hope and fear for discriminative training of statistical translation models. Journal of Machine Learning Research. 13(1). 1159–1187.51 indexed citations
11.
Zhang, Hui & David Chiang. (2012). An Exploration of Forest-to-String Translation: Does Translation Help or Hurt Parsing?. Meeting of the Association for Computational Linguistics. 317–321.2 indexed citations
12.
Hovy, Dirk, Ashish Vaswani, Stephen Tratz, David Chiang, & Eduard Hovy. (2011). Models and Training for Unsupervised Preposition Sense Disambiguation. Meeting of the Association for Computational Linguistics. 323–328.7 indexed citations
13.
Cai, Shu, David Chiang, & Yoav Goldberg. (2011). Language-Independent Parsing with Empty Elements. Meeting of the Association for Computational Linguistics. 212–216.37 indexed citations
14.
Pauls, Adam, Dan Klein, David Chiang, & Kevin Knight. (2010). Unsupervised Syntactic Alignment with Inversion Transduction Grammars. North American Chapter of the Association for Computational Linguistics. 118–126.12 indexed citations
15.
Vaswani, Ashish, Adam Pauls, & David Chiang. (2010). Efficient Optimization of an MDL-Inspired Objective Function for Unsupervised Part-Of-Speech Tagging. Meeting of the Association for Computational Linguistics. 209–214.8 indexed citations
16.
Wu, Dekai & David Chiang. (2009). Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation (SSST-3) at NAACL HLT 2009. North American Chapter of the Association for Computational Linguistics.2 indexed citations
17.
Wu, Dekai & David Chiang. (2008). Proceedings of SSST-2, Second Workshop on Syntax and Structure in Statistical Translation. Meeting of the Association for Computational Linguistics.1 indexed citations
18.
Chan, Yee Seng, Hwee Tou Ng, & David Chiang. (2007). Word Sense Disambiguation Improves Statistical Machine Translation. National University of Singapore. 33–40.196 indexed citations
19.
Wu, Dekai & David Chiang. (2007). NAACL-HLT 2007/ AMTA Workshop on Syntax and Structure in Statistical Translation (SSST): Held in conjunction with NAACL-HLT 2007, Rochester, New York, USA, 26 April 2007. North American Chapter of the Association for Computational Linguistics.2 indexed citations
20.
Chiang, David, Adam Lopez, Nitin Madnani, et al.. (2005). Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing.22 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.