Marco Dinarelli

555 total citations
24 papers, 222 citations indexed

About

Marco Dinarelli is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Marco Dinarelli has authored 24 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 3 papers in Signal Processing and 1 paper in Computer Vision and Pattern Recognition. Recurrent topics in Marco Dinarelli's work include Natural Language Processing Techniques (22 papers), Topic Modeling (20 papers) and Speech and dialogue systems (12 papers). Marco Dinarelli is often cited by papers focused on Natural Language Processing Techniques (22 papers), Topic Modeling (20 papers) and Speech and dialogue systems (12 papers). Marco Dinarelli collaborates with scholars based in France, Italy and Canada. Marco Dinarelli's co-authors include Giuseppe Riccardi, Alessandro Moschitti, Silvia Quarteroni, Sara Tonelli, Christian Raymond, Sophie Rosset, Renato De Mori, Patrick Lehnen, Stefan Hahn and Florence Lefèvre and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).

In The Last Decade

Marco Dinarelli

21 papers receiving 198 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Dinarelli France 8 214 27 19 5 5 24 222
Paden Tomasello France 5 140 0.7× 49 1.8× 22 1.2× 3 0.6× 11 2.2× 10 153
Marsal Gavaldà United States 8 167 0.8× 24 0.9× 11 0.6× 7 1.4× 3 0.6× 19 177
Guillaume Wisniewski France 8 155 0.7× 11 0.4× 18 0.9× 3 0.6× 4 0.8× 40 162
Shuoyang Ding United States 7 127 0.6× 29 1.1× 34 1.8× 6 1.2× 2 0.4× 12 134
Ankur Gandhe United States 8 207 1.0× 54 2.0× 14 0.7× 7 1.4× 2 0.4× 27 213
Elizabeth Salesky United States 7 184 0.9× 17 0.6× 32 1.7× 7 1.4× 4 0.8× 20 192
Zhouxing Shi United States 7 106 0.5× 14 0.5× 12 0.6× 10 2.0× 4 0.8× 11 116
Mohammad Sadegh Rasooli United States 10 249 1.2× 11 0.4× 31 1.6× 15 3.0× 5 1.0× 22 261
Sebastian Stüker Germany 8 235 1.1× 49 1.8× 57 3.0× 3 0.6× 5 1.0× 16 248
Mathias Rossignol France 6 68 0.3× 25 0.9× 22 1.2× 13 2.6× 4 0.8× 10 102

Countries citing papers authored by Marco Dinarelli

Since Specialization
Citations

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

Fields of papers citing papers by Marco Dinarelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Dinarelli

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Dinarelli. A scholar is included among the top collaborators of Marco Dinarelli 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 Marco Dinarelli. Marco Dinarelli 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.
Parcollet, Titouan, et al.. (2024). Open Implementation and Study of Best-RQ for Speech Processing. 460–464.
2.
Dinarelli, Marco, et al.. (2022). Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 4557–4572. 6 indexed citations
3.
Dinarelli, Marco, et al.. (2022). Toward Low-Cost End-to-End Spoken Language Understanding. Interspeech 2022. 2728–2732. 3 indexed citations
4.
Nguyen, Ha-Thanh, Hang Le, Natalia Tomashenko, et al.. (2021). LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech. arXiv (Cornell University). 34 indexed citations
5.
Dinarelli, Marco, et al.. (2020). Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level Annotation. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
6.
Dinarelli, Marco, et al.. (2020). A Data Efficient End-to-End Spoken Language Understanding Architecture. 99. 8519–8523. 7 indexed citations
7.
Dinarelli, Marco, et al.. (2017). Label-Dependency Coding in Simple Recurrent Networks for Spoken Language Understanding. 2491–2495. 5 indexed citations
8.
Tellier, Isabelle, et al.. (2017). Apports des analyses syntaxiques pour la détection automatique de mentions dans un corpus de français oral. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
9.
Tian, Tian, Marco Dinarelli, & Isabelle Tellier. (2015). Data Adaptation for Named Entity Recognition on Tweets with Features-Rich CRF. 68–71. 3 indexed citations
10.
Dinarelli, Marco, et al.. (2013). LIMSI $@$ WMT13. Workshop on Statistical Machine Translation. 62–69. 7 indexed citations
11.
Dinarelli, Marco & Sophie Rosset. (2011). Models Cascade for Tree-Structured Named Entity Detection. International Joint Conference on Natural Language Processing. 1269–1278. 10 indexed citations
12.
Dinarelli, Marco, Alessandro Moschitti, & Giuseppe Riccardi. (2011). Discriminative Reranking for Spoken Language Understanding. IEEE Transactions on Audio Speech and Language Processing. 20(2). 526–539. 16 indexed citations
13.
Dinarelli, Marco, Evgeny A. Stepanov, Sebastian Varges, & Giuseppe Riccardi. (2010). The LUNA Spoken Dialogue System: Beyond utterance classification. 2 indexed citations
14.
Dinarelli, Marco, Alessandro Moschitti, & Giuseppe Riccardi. (2010). Hypotheses selection for re-ranking semantic annotations. Institutional Research Information System (Università degli Studi di Trento). 14. 407–411. 1 indexed citations
15.
Dinarelli, Marco, Alessandro Moschitti, & Giuseppe Riccardi. (2009). Concept segmentation and labeling for conversational speech. 2747–2750. 6 indexed citations
16.
Dinarelli, Marco, Alessandro Moschitti, & Giuseppe Riccardi. (2009). Re-ranking models based-on small training data for spoken language understanding. Institutional Research Information System (Università degli Studi di Trento). 3. 1076–1076. 9 indexed citations
17.
Quarteroni, Silvia, Marco Dinarelli, & Giuseppe Riccardi. (2009). Ontology-based grounding of Spoken Language Understanding. Institutional Research Information System (Università degli Studi di Trento). 18. 438–443. 2 indexed citations
18.
Quarteroni, Silvia, Giuseppe Riccardi, & Marco Dinarelli. (2009). What's in an ontology for spoken language understanding. 1023–1026. 8 indexed citations
19.
Dinarelli, Marco, Alessandro Moschitti, & Giuseppe Riccardi. (2009). Re-ranking models for spoken language understanding. Institutional Research Information System (Università degli Studi di Trento). 202–210. 10 indexed citations
20.
Bisazza, Arianna, Marco Dinarelli, Silvia Quarteroni, et al.. (2008). Semantic annotations for conversational speech: From speech transcriptions to predicate argument structures. Institutional Research Information System (Università degli Studi di Trento). 65–68. 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.

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