H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,589 65 World Ranking 8563 National Ranking 154

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Natural language processing
  • Machine learning

His primary areas of investigation include Natural language processing, Artificial intelligence, Machine translation, Word and Representation. His Part of speech and Language model study in the realm of Natural language processing connects with subjects such as Process, Identity and Encoder. His research in the fields of Character overlaps with other disciplines such as Layer.

Yonatan Belinkov has included themes like Speech recognition, Translation, Convolutional neural network and Robustness in his Machine translation study. His Word study combines topics from a wide range of disciplines, such as Context, Word order, Classifier, Sentence and Deep learning. Representation is often connected to Recurrent neural network in his work.

His most cited work include:

  • Linguistic Knowledge and Transferability of Contextual Representations (335 citations)
  • Synthetic and Natural Noise Both Break Neural Machine Translation (287 citations)
  • Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks (245 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Artificial neural network, Machine translation and Machine learning. His Artificial intelligence study frequently draws parallels with other fields, such as Speech recognition. His Natural language processing research includes elements of Representation, Context, Word and Arabic.

Yonatan Belinkov works mostly in the field of Artificial neural network, limiting it down to topics relating to Ranking and, in certain cases, F1 score. His Machine translation research incorporates elements of Interpretability, Rule-based machine translation and Robustness. His Machine learning research is multidisciplinary, incorporating elements of Adversarial system, Training set and Natural language inference.

He most often published in these fields:

  • Artificial intelligence (77.57%)
  • Natural language processing (53.27%)
  • Artificial neural network (25.23%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (77.57%)
  • Natural language processing (53.27%)
  • Language model (16.82%)

In recent papers he was focusing on the following fields of study:

Yonatan Belinkov spends much of his time researching Artificial intelligence, Natural language processing, Language model, Machine learning and Transfer of learning. His Artificial intelligence study focuses mostly on Transformer, Sentence, Artificial neural network, Machine translation and Interpretability. His work investigates the relationship between Sentence and topics such as Word that intersect with problems in Encoding.

His research investigates the connection between Artificial neural network and topics such as Deep learning that intersect with problems in Algorithm and Dependency. Yonatan Belinkov has researched Natural language processing in several fields, including Interpretation and Representation. His studies deal with areas such as Schema, Cognitive psychology and Task as well as Language model.

Between 2019 and 2021, his most popular works were:

  • A Constructive Prediction of the Generalization Error Across Scales (28 citations)
  • End-to-End Bias Mitigation by Modelling Biases in Corpora (27 citations)
  • Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (26 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

Yonatan Belinkov mainly focuses on Artificial intelligence, Natural language processing, Causal mediation, Gender bias and Language model. His work on Machine translation, Interpretability and Deep neural networks is typically connected to Power and Similarity analysis as part of general Artificial intelligence study, connecting several disciplines of science. His studies in Natural language processing integrate themes in fields like Word, Representation, Relevance and Set.

Yonatan Belinkov undertakes multidisciplinary investigations into Causal mediation and Cognitive psychology in his work.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Synthetic and Natural Noise Both Break Neural Machine Translation

Yonatan Belinkov;Yonatan Bisk.
international conference on learning representations (2018)

396 Citations

Linguistic Knowledge and Transferability of Contextual Representations

Nelson F. Liu;Matt Gardner;Yonatan Belinkov;Matthew E. Peters.
north american chapter of the association for computational linguistics (2019)

335 Citations

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

Yossi Adi;Einat Kermany;Yonatan Belinkov;Ofer Lavi.
international conference on learning representations (2016)

245 Citations

What do neural machine translation models learn about morphology

Yonatan Belinkov;Nadir Durrani;Fahim Dalvi;Hassan Sajjad.
meeting of the association for computational linguistics (2017)

243 Citations

Analysis Methods in Neural Language Processing: A Survey

Yonatan Belinkov;James R. Glass.
Transactions of the Association for Computational Linguistics (2019)

204 Citations

Analyzing the Structure of Attention in a Transformer Language Model

Jesse Vig;Yonatan Belinkov.
meeting of the association for computational linguistics (2019)

125 Citations

Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks

Yonatan Belinkov;Lluís Màrquez;Hassan Sajjad;Nadir Durrani.
international joint conference on natural language processing (2017)

115 Citations

End-to-End Bias Mitigation by Modelling Biases in Corpora

Rabeeh Karimi Mahabadi;Yonatan Belinkov;James Henderson.
meeting of the association for computational linguistics (2020)

97 Citations

What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models.

Fahim Dalvi;Nadir Durrani;Hassan Sajjad;Yonatan Belinkov.
national conference on artificial intelligence (2019)

80 Citations

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Yonatan Belinkov;James R. Glass.
neural information processing systems (2017)

78 Citations

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