Raj Dabre
Impact in
- Artificial Intelligence top 2%
- Natural Language Processing Techniques
- Topic Modeling
- Speech Recognition and Synthesis
- Text Readability and Simplification
- Speech and dialogue systems
-
- Multimodal Machine Learning Applications
Papers in
-
- Natural Language Processing Techniques 63
- Topic Modeling 50
- Text Readability and Simplification 9
- Speech Recognition and Synthesis 8
- Speech and dialogue systems 7
-
- Multimodal Machine Learning Applications 18
- Co-authors
- Chenhui Chu (14 shared papers)Sadao Kurohashi (18 shared papers)Atsushi Fujita (8 shared papers)Anoop Kunchukuttan (16 shared papers)Eiichiro Sumita (9 shared papers)Mitesh M. Khapra (7 shared papers)Ondřej Bojar (5 shared papers)Tetsuji Nakagawa (1 shared paper)
- Journals
- ACM Computing Surveys (2 papers)Language Resources and Evaluation (2 papers)Machine Translation (1 paper)ACM Transactions on Asian and Low-Resource Language Information Processing (2 papers)Findings of the Association for Computational Linguistics: ACL 2022 (1 paper)
- Partner nations
- JapanIndiaUnited Kingdom
In The Last Decade
Raj Dabre
60 papers receiving 489 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 492
- Computer Vision and Pattern Recognition 200
- Signal Processing 38
- Health Informatics 4
- Language and Linguistics 31
Countries citing papers authored by Raj Dabre
This map shows the geographic impact of Raj Dabre'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 Raj Dabre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raj Dabre more than expected).
Fields of papers citing papers by Raj Dabre
This network shows the impact of papers produced by Raj Dabre. 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 Raj Dabre. The network helps show where Raj Dabre may publish in the future.
Co-authors
The 25 scholars most cited alongside Raj Dabre, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 115 | |
| 2 | 2021 | 34 | |
| 3 | 2022 | 32 | |
| 4 | 2019 | 28 | |
| 5 | 2019 | 27 | |
| 6 | 2019 | 25 | |
| 7 | An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation | 2017 | 24 |
| 8 | 2019 | 23 | |
| 9 | 2022 | 19 | |
| 10 | 2020 | 16 | |
| 11 | 2020 | 14 | |
| 12 | 2015 | 14 | |
| 13 | 2022 | 12 | |
| 14 | 2025 | 10 | |
| 15 | 2023 | 9 | |
| 16 | 2019 | 9 | |
| 17 | Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation | 2019 | 8 |
| 18 | 2023 | 8 | |
| 19 | 2018 | 7 | |
| 20 | 2024 | 6 |
About Raj Dabre
Raj Dabre is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Language and Linguistics, Signal Processing and Experimental and Cognitive Psychology, having authored 70 papers that have together received 543 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (63 papers), Topic Modeling (50 papers), Multimodal Machine Learning Applications (18 papers), Text Readability and Simplification (9 papers), Speech Recognition and Synthesis (8 papers), Speech and dialogue systems (7 papers), Translation Studies and Practices (5 papers) and Language, Linguistics, Cultural Analysis (2 papers). The work is most often cited by research in Artificial Intelligence (492 citations), Computer Vision and Pattern Recognition (200 citations), Signal Processing (38 citations), Health Informatics (4 citations) and Language and Linguistics (31 citations). Raj Dabre has collaborated with scholars based in Japan, India and United Kingdom. Frequent co-authors include Chenhui Chu, Sadao Kurohashi, Atsushi Fujita, Anoop Kunchukuttan, Eiichiro Sumita, Mitesh M. Khapra, Ondřej Bojar, Tetsuji Nakagawa, Ratish Puduppully and Chenchen Ding. Their work appears in journals such as ACM Computing Surveys, Language Resources and Evaluation, Machine Translation, ACM Transactions on Asian and Low-Resource Language Information Processing and Findings of the Association for Computational Linguistics: ACL 2022.
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.