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.
Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data
20171.0k citationsAlexis Conneau, Douwe Kiela et al.arXiv (Cornell University)profile →
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 ·
hero ref
This map shows the geographic impact of Loïc Barrault'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 Loïc Barrault with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Loïc Barrault more than expected).
This network shows the impact of papers produced by Loïc Barrault. 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 Loïc Barrault. The network helps show where Loïc Barrault may publish in the future.
Co-authorship network of co-authors of Loïc Barrault
This figure shows the co-authorship network connecting the top 25 collaborators of Loïc Barrault.
A scholar is included among the top collaborators of Loïc Barrault 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 Loïc Barrault. Loïc Barrault is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Specia, Lucia, Loïc Barrault, Ozan Çağlayan, et al.. (2020). Grounded Sequence to Sequence Transduction. IEEE Journal of Selected Topics in Signal Processing. 14(3). 577–591.2 indexed citations
11.
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 →
12.
Conneau, Alexis, Douwe Kiela, Holger Schwenk, Loïc Barrault, & Antoine Bordes. (2017). Supervised Learning of Universal Sentence Representations from Natural\n Language Inference Data. arXiv (Cornell University).1002 indexed citations breakdown →
Federico, Marcello, Nicola Bertoldi, Mauro Cettolo, et al.. (2014). THE MATECAT TOOL. International Conference on Computational Linguistics. 129–132.37 indexed citations
15.
Schwenk, Holger, Fethi Bougares, & Loïc Barrault. (2014). Efficient training strategies for deep neural network language models.. SPIRE - Sciences Po Institutional REpository.4 indexed citations
Afli, Haithem, Loïc Barrault, & Holger Schwenk. (2013). Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction. SPIRE - Sciences Po Institutional REpository.3 indexed citations
18.
Servan, Christophe, Patrik Lambert, Anthony Rousseau, Holger Schwenk, & Loïc Barrault. (2012). LIUM's SMT Machine Translation Systems for WMT 2012. SPIRE - Sciences Po Institutional REpository. 369–373.
19.
Shah, Kashif, Loïc Barrault, & Holger Schwenk. (2012). A General Framework to Weight Heterogeneous Parallel Data for Model Adaptation in Statistical MT.. Conference of the Association for Machine Translation in the Americas.1 indexed citations
20.
Rousseau, Anthony, Loïc Barrault, Paul Deléglise, & Yannick Estève. (2009). LIUM's Statistical Machine Translation Systems for IWSLT 2009. SPIRE - Sciences Po Institutional REpository.1 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.