Arianna Bisazza

2.4k total citations
47 papers, 585 citations indexed

About

Arianna Bisazza is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Arianna Bisazza has authored 47 papers receiving a total of 585 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Arianna Bisazza's work include Natural Language Processing Techniques (44 papers), Topic Modeling (43 papers) and Speech and dialogue systems (12 papers). Arianna Bisazza is often cited by papers focused on Natural Language Processing Techniques (44 papers), Topic Modeling (43 papers) and Speech and dialogue systems (12 papers). Arianna Bisazza collaborates with scholars based in Netherlands, Italy and Czechia. Arianna Bisazza's co-authors include Marcello Federico, Christof Monz, Mauro Cettolo, Luisa Bentivogli, Ke Tran, Gertjan van Noord, Christian Hardmeier, Gosse Bouma, Ahmet Üstün and Wouter Weerkamp and has published in prestigious journals such as Computational Linguistics, Language Resources and Evaluation and Computer Speech & Language.

In The Last Decade

Arianna Bisazza

44 papers receiving 513 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Arianna Bisazza Netherlands 11 553 121 35 33 31 47 585
Santanu Pal India 12 596 1.1× 148 1.2× 30 0.9× 28 0.8× 46 1.5× 55 631
Mark Fishel Estonia 12 546 1.0× 138 1.1× 62 1.8× 28 0.8× 39 1.3× 46 595
Katharina Kann United States 14 505 0.9× 103 0.9× 16 0.5× 15 0.5× 22 0.7× 45 538
Huihsin Tseng United States 6 392 0.7× 73 0.6× 20 0.6× 46 1.4× 49 1.6× 7 432
Ondřej Dušek Czechia 12 396 0.7× 66 0.5× 24 0.7× 33 1.0× 30 1.0× 62 443
Shijie Wu United States 9 552 1.0× 145 1.2× 14 0.4× 11 0.3× 30 1.0× 12 577
Sandipan Dandapat India 12 422 0.8× 72 0.6× 16 0.5× 12 0.4× 42 1.4× 48 464
Joakim Nivre United States 3 426 0.8× 32 0.3× 23 0.7× 30 0.9× 27 0.9× 4 450
Matthias Huck Germany 13 892 1.6× 188 1.6× 26 0.7× 61 1.8× 44 1.4× 54 928
Katsuhito Sudoh Japan 15 791 1.4× 157 1.3× 21 0.6× 41 1.2× 37 1.2× 96 829

Countries citing papers authored by Arianna Bisazza

Since Specialization
Citations

This map shows the geographic impact of Arianna Bisazza'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 Arianna Bisazza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arianna Bisazza more than expected).

Fields of papers citing papers by Arianna Bisazza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arianna Bisazza. 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 Arianna Bisazza. The network helps show where Arianna Bisazza may publish in the future.

Co-authorship network of co-authors of Arianna Bisazza

This figure shows the co-authorship network connecting the top 25 collaborators of Arianna Bisazza. A scholar is included among the top collaborators of Arianna Bisazza 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 Arianna Bisazza. Arianna Bisazza is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Toral, Antonio, et al.. (2024). Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation. Transactions of the Association for Computational Linguistics. 12. 392–410. 3 indexed citations
2.
Monz, Christof, et al.. (2024). The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models. UvA-DARE (University of Amsterdam). 1691–1706.
3.
Fernández, Raquel, et al.. (2024). Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation. UvA-DARE (University of Amsterdam). 6037–6053. 2 indexed citations
4.
Üstün, Ahmet, Arianna Bisazza, Gosse Bouma, Gertjan van Noord, & Sebastian Ruder. (2022). Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer. 7934–7949. 9 indexed citations
5.
Bisazza, Arianna, et al.. (2020). Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen’s Submission to WMT-2020. University of Groningen research database (University of Groningen / Centre for Information Technology). 126–133. 3 indexed citations
6.
Bisazza, Arianna, et al.. (2018). Evaluation of Machine Translation Performance Across Multiple Genres and Languages. Language Resources and Evaluation. 6 indexed citations
7.
Tran, Ke, Arianna Bisazza, & Christof Monz. (2018). The Importance of Being Recurrent for Modeling Hierarchical Structure. UvA-DARE (University of Amsterdam). 4731–4736. 64 indexed citations
8.
Bisazza, Arianna, et al.. (2016). A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation. UvA-DARE (University of Amsterdam). 43–50. 9 indexed citations
9.
Bisazza, Arianna, et al.. (2016). Measuring the effect of conversational aspects on machine translation quality. UvA-DARE (University of Amsterdam). 2571–2581. 6 indexed citations
10.
Bentivogli, Luisa, Arianna Bisazza, Mauro Cettolo, & Marcello Federico. (2016). . UvA-DARE (University of Amsterdam). 168 indexed citations
11.
Bisazza, Arianna, et al.. (2015). Five Shades of Noise: Analyzing Machine Translation Errors in User-Generated Text. UvA-DARE (University of Amsterdam). 28–37. 7 indexed citations
12.
Bisazza, Arianna & Marcello Federico. (2013). Efficient Solutions for Word Reordering in German-English Phrase-Based Statistical Machine Translation. Workshop on Statistical Machine Translation. 440–451. 5 indexed citations
13.
Bisazza, Arianna & Marcello Federico. (2012). Modified Distortion Matrices for Phrase-Based Statistical Machine Translation. Data Archiving and Networked Services (DANS). 1. 478–487. 4 indexed citations
14.
Bisazza, Arianna, et al.. (2012). FBK's Machine Translation Systems for IWSLT 2012's TED Lectures. IWSLT. 61–68. 2 indexed citations
15.
Bisazza, Arianna & Marcello Federico. (2012). Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation. Data Archiving and Networked Services (DANS). 439–448. 11 indexed citations
16.
Bisazza, Arianna, Fabio Brugnara, Daniele Falavigna, et al.. (2011). FBK @ IWSLT 2011.. IWSLT. 86–93. 4 indexed citations
17.
Hardmeier, Christian, Arianna Bisazza, & Marcello Federico. (2010). FBK at WMT 2010: Word Lattices for Morphological Reduction and Chunk-Based Reordering. Edinburgh Research Explorer (University of Edinburgh). 88–92. 12 indexed citations
18.
Bisazza, Arianna & Marcello Federico. (2010). Chunk-Based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation. Workshop on Statistical Machine Translation. 235–243. 10 indexed citations
19.
Bertoldi, Nicola, et al.. (2009). FBK @ IWSLT 2009. IWSLT. 37–44.
20.
Bisazza, Arianna & Marcello Federico. (2009). Morphological Pre-Processing for Turkish to English Statistical Machine Translation. IWSLT. 13. 129–135. 23 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.

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