Goldberg, Yoav

Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages

Ravfogel, S., Goldberg, Y., & Linzen, T. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 39

Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks

Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 36

Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks

Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 36

Analysis of sentence embedding models using prediction tasks in natural language processing

Adi, Y., Kermany, E., Belinkov, Y., Lavi, O., & Goldberg, Y. (2017) · IBM Journal of Research and Development

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 21

Thinking Like Transformers

Weiss, G., Goldberg, Y., & Yahav, E. (2021) · arXiv

semigroups and automata theoryTopic Modeling

Cited by 18

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben-Zaken, E., Ravfogel, S., & Goldberg, Y. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 11