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.
Universal Sentence Encoder for English
2018705 citationsDaniel Cer, Yinfei Yang et al.profile →
2014307 citationsEneko Agirre, Carmen Banea et al.profile →
Bilingual Word Embeddings for Phrase-Based Machine Translation
2013307 citationsWill Y. Zou, Richard Socher et al.profile →
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation
2016296 citationsEneko Agirre, Carmen Banea et al.profile →
SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability
2015294 citationsEneko Agirre, Carmen Banea et al.profile →
Language-agnostic BERT Sentence Embedding
2022177 citationsFangxiaoyu Feng, Yinfei Yang et al.Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)profile →
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models
2022138 citationsJianmo Ni, Gustavo Hernández Ábrego et al.Findings of the Association for Computational Linguistics: ACL 2022profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Daniel Cer'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 Daniel Cer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Cer more than expected).
This network shows the impact of papers produced by Daniel Cer. 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 Daniel Cer. The network helps show where Daniel Cer may publish in the future.
Co-authorship network of co-authors of Daniel Cer
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Cer.
A scholar is included among the top collaborators of Daniel Cer 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 Daniel Cer. Daniel Cer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ni, Jianmo, Gustavo Hernández Ábrego, Noah Constant, et al.. (2022). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of the Association for Computational Linguistics: ACL 2022. 1864–1874.138 indexed citations breakdown →
4.
Feng, Fangxiaoyu, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, & Wei Wang. (2022). Language-agnostic BERT Sentence Embedding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 878–891.177 indexed citations breakdown →
Cer, Daniel, Yinfei Yang, Sheng-yi Kong, et al.. (2018). Universal Sentence Encoder for English. 169–174.705 indexed citations breakdown →
8.
Agirre, Eneko, Carmen Banea, Daniel Cer, et al.. (2016). SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation. 497–511.296 indexed citations breakdown →
9.
Cer, Daniel, Christopher D. Manning, & Dan Jurafsky. (2013). Positive Diversity Tuning for Machine Translation System Combination. Workshop on Statistical Machine Translation. 320–328.4 indexed citations
10.
Green, Spence, Sida Wang, Daniel Cer, & Christopher D. Manning. (2013). Fast and Adaptive Online Training of Feature-Rich Translation Models. Meeting of the Association for Computational Linguistics. 311–321.25 indexed citations
11.
Green, Spence, Daniel Cer, Rob Voigt, et al.. (2013). Feature-Rich Phrase-based Translation: Stanford University's Submission to the WMT 2013 Translation Task. Workshop on Statistical Machine Translation. 148–153.5 indexed citations
12.
Zou, Will Y., Richard Socher, Daniel Cer, & Christopher D. Manning. (2013). Bilingual Word Embeddings for Phrase-Based Machine Translation. 1393–1398.307 indexed citations breakdown →
13.
Agirre, Eneko, Daniel Cer, Mona Diab, Aitor González-Agirre, & Weiwei Guo. (2013). *SEM 2013 shared task: Semantic Textual Similarity. Joint Conference on Lexical and Computational Semantics. 1. 32–43.232 indexed citations
Wang, Mengqiu & Daniel Cer. (2012). Stanford: Probabilistic Edit Distance Metrics for STS. Joint Conference on Lexical and Computational Semantics. 1. 648–654.3 indexed citations
16.
Cer, Daniel, Christopher D. Manning, & Daniel Jurafsky. (2010). The Best Lexical Metric for Phrase-Based Statistical MT System Optimization. North American Chapter of the Association for Computational Linguistics. 555–563.37 indexed citations
17.
Cer, Daniel, Marie-Catherine de Marneffe, Daniel Jurafsky, & Christopher D. Manning. (2010). Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy.. Language Resources and Evaluation.88 indexed citations
18.
Cer, Daniel, Michel Galley, Daniel Jurafsky, & Christopher D. Manning. (2010). Phrasal: a toolkit for statistical machine translation with facilities for extraction and incorporation of arbitrary model features. North American Chapter of the Association for Computational Linguistics. 9–12.25 indexed citations
Marneffe, Marie-Catherine de, Trond Grenager, Bill MacCartney, et al.. (2007). Robust Graph Alignment Methods for Textual Inference and Machine Reading.. National Conference on Artificial Intelligence. 36–42.2 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.