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.
Findings of the 2019 Conference on Machine Translation (WMT19)
2019256 citationsLoïc Barrault, Ondřej Bojar 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 Matthias Huck'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 Matthias Huck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Huck more than expected).
This network shows the impact of papers produced by Matthias Huck. 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 Matthias Huck. The network helps show where Matthias Huck may publish in the future.
Co-authorship network of co-authors of Matthias Huck
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Huck.
A scholar is included among the top collaborators of Matthias Huck 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 Matthias Huck. Matthias Huck is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Barrault, Loïc, Ondřej Bojar, Marta R. Costa‐jussà, et al.. (2019). Findings of the 2019 Conference on Machine Translation (WMT19). 1–61.256 indexed citations breakdown →
3.
Huck, Matthias, et al.. (2018). Neural Morphological Tagging of Lemma Sequences for Machine Translation. Conference of the Association for Machine Translation in the Americas. 1. 39–53.5 indexed citations
4.
Williams, Philip, Rico Sennrich, Maria Nădejde, et al.. (2016). Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers.4 indexed citations
5.
Kordoni, Valia, Antal van den Bosch, Katia Lida Kermanidis, et al.. (2016). Enhancing Access to Online Education: Quality Machine Translation of MOOC Content. Language Resources and Evaluation. 16–22.5 indexed citations
6.
Williams, Philip, Rico Sennrich, Maria Nădejde, Matthias Huck, & Philipp Koehn. (2015). Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015.3 indexed citations
7.
Huck, Matthias, Alexandra Birch, & Barry Haddow. (2015). Proceedings of MT Summit XV, vol.1: MT Researchers' Track.1 indexed citations
8.
Williams, Philip, Rico Sennrich, Maria Nădejde, et al.. (2014). Proceedings of the Ninth Workshop on Statistical Machine Translation.30 indexed citations
9.
Huck, Matthias, David Vilar, Markus Freitag, & Hermann Ney. (2013). A Performance Study of Cube Pruning for Large-Scale Hierarchical Machine Translation. RWTH Publications (RWTH Aachen). 29–38.3 indexed citations
10.
Peitz, Stephan, Saab Mansour, Matthias Huck, et al.. (2013). Joint WMT 2013 Submission of the QUAERO Project. RWTH Publications (RWTH Aachen). 185–192.3 indexed citations
11.
Huck, Matthias, et al.. (2013). A Phrase Orientation Model for Hierarchical Machine Translation. RWTH Publications (RWTH Aachen). 452–463.14 indexed citations
12.
Huck, Matthias & Hermann Ney. (2012). Insertion and Deletion Models for Statistical Machine Translation. RWTH Publications (RWTH Aachen). 347–351.4 indexed citations
13.
Huck, Matthias & Hermann Ney. (2012). Pivot Lightly-Supervised Training for Statistical Machine Translation. RWTH Publications (RWTH Aachen).5 indexed citations
14.
Peitz, Stephan, Saab Mansour, Markus Freitag, et al.. (2012). The RWTH Aachen Speech Recognition and Machine Translation System for IWSLT 2012. RWTH Publications (RWTH Aachen). 69–76.2 indexed citations
15.
Wuebker, Joern, Matthias Huck, Stephan Peitz, et al.. (2012). Jane 2: Open Source Phrase-based and Hierarchical Statistical Machine Translation. RWTH Publications (RWTH Aachen). 483–492.33 indexed citations
16.
Huck, Matthias, David Vilar, Dan J. Stein, & Hermann Ney. (2011). Lightly-Supervised Training for Hierarchical Phrase-Based Machine Translation. RWTH Publications (RWTH Aachen). 91–96.8 indexed citations
17.
Huck, Matthias, Saab Mansour, Simon Wiesler, & Hermann Ney. (2011). Lexicon models for hierarchical phrase-based machine translation.. RWTH Publications (RWTH Aachen). 191–198.6 indexed citations
18.
Huck, Matthias, David Vilar, D. L. Stein, & Hermann Ney. (2011). Advancements in Arabic-to-English Hierarchical Machine Translation. RWTH Publications (RWTH Aachen).4 indexed citations
19.
Huck, Matthias, Joern Wuebker, Christoph Schmidt, et al.. (2010). The RWTH Aachen Machine Translation System for WMT 2010. RWTH Publications (RWTH Aachen). 193–199.19 indexed citations
20.
Huck, Matthias, et al.. (2010). A Comparison of Various Types of Extended Lexicon Models for Statistical Machine Translation. RWTH Publications (RWTH Aachen).11 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.