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.
Moses
20073.2k citationsPhilipp Koehn, Ondřej Bojar et al.profile →
This map shows the geographic impact of Ondřej Bojar'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 Ondřej Bojar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ondřej Bojar more than expected).
This network shows the impact of papers produced by Ondřej Bojar. 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 Ondřej Bojar. The network helps show where Ondřej Bojar may publish in the future.
Co-authorship network of co-authors of Ondřej Bojar
This figure shows the co-authorship network connecting the top 25 collaborators of Ondřej Bojar.
A scholar is included among the top collaborators of Ondřej Bojar 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 Ondřej Bojar. Ondřej Bojar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bojar, Ondřej, et al.. (2021). Lost in Interpreting: Speech Translation from Source or Interpreter?.5 indexed citations
6.
Polák, Peter, et al.. (2019). Large Corpus of Czech Parliament Plenary Hearings. Language Resources and Evaluation. 6363–6367.4 indexed citations
7.
Kocmi, Tom & Ondřej Bojar. (2017). An Exploration of Word Embedding Initialization in Deep-Learning Tasks. 56–64.1 indexed citations
8.
Kamran, Amir, et al.. (2016). Enriching Source for English-to-Urdu Machine Translation.. International Conference on Computational Linguistics. 54–63.1 indexed citations
9.
Bojar, Ondřej, et al.. (2015). Giving a Sense: A Pilot Study in Concept Annotation from Multiple Resources. 88–94.
Tamchyna, Aleš, et al.. (2012). Selecting Data for English-to-Czech Machine Translation. Workshop on Statistical Machine Translation. 374–381.5 indexed citations
12.
Bojar, Ondřej, et al.. (2012). Probes in a Taxonomy of Factored Phrase-Based Models. Workshop on Statistical Machine Translation. 253–260.10 indexed citations
13.
Bojar, Ondřej, et al.. (2012). Automatic MT Error Analysis: Hjerson Helping Addicter. Language Resources and Evaluation. 2158–2163.5 indexed citations
14.
Bojar, Ondřej & Dekai Wu. (2012). Towards a Predicate-Argument Evaluation for MT. Meeting of the Association for Computational Linguistics. 30–38.6 indexed citations
15.
Mareček, David, et al.. (2011). Two-step translation with grammatical post-processing. Workshop on Statistical Machine Translation. 426–432.21 indexed citations
16.
Bojar, Ondřej, Adam Liska, & Zdeněk Žabokrtský. (2010). Evaluating Utility of Data Sources in a Large Parallel Czech-English Corpus CzEng 0.9. Language Resources and Evaluation.3 indexed citations
17.
Bojar, Ondřej, et al.. (2009). Statistical Machine Translation Between Related and Unrelated Languages.. 31–36.5 indexed citations
18.
Bojar, Ondřej, et al.. (2006). Czech-English Word Alignment. Language Resources and Evaluation. 1236–1239.13 indexed citations
19.
Bojar, Ondřej, et al.. (2005). An MT System Recycled. 3(1). 380–387.1 indexed citations
20.
Bojar, Ondřej. (2003). Towards Automatic Extraction of Verb Frames. The Prague Bulletin of Mathematical Linguistics. 101–120.4 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.