Neural Word Embedding as Implicit Matrix Factorization O Levy, Y Goldberg Advances in Neural Information Processing Systems, 2177-2185, 2014 | 2522 | 2014 |
word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method Y Goldberg, O Levy arXiv preprint arXiv:1402.3722, 2014 | 2289 | 2014 |
Neural Network Methods for Natural Language Processing Y Goldberg Synthesis Lectures on Human Language Technologies 10 (1), 1-309, 2017 | 1794* | 2017 |
Improving distributional similarity with lessons learned from word embeddings O Levy, Y Goldberg, I Dagan Transactions of the Association for Computational Linguistics 3, 211-225, 2015 | 1780 | 2015 |
Dependency-Based Word Embeddings. O Levy, Y Goldberg ACL (2), 302-308, 2014 | 1703 | 2014 |
Universal dependencies v1: A multilingual treebank collection J Nivre, MC de Marneffe, F Ginter, Y Goldberg, J Hajic, CD Manning, ... Proceedings of the 10th International Conference on Language Resources and …, 2016 | 1622 | 2016 |
A primer on neural network models for natural language processing Y Goldberg Journal of Artificial Intelligence Research 57, 345-420, 2016 | 1494 | 2016 |
Assessing the ability of LSTMs to learn syntax-sensitive dependencies T Linzen, E Dupoux, Y Goldberg Transactions of the Association for Computational Linguistics 4, 521-535, 2016 | 964 | 2016 |
Linguistic regularities in sparse and explicit word representations O Levy, Y Goldberg CoNLL-2014, 171, 2014 | 792 | 2014 |
Simple and accurate dependency parsing using bidirectional LSTM feature representations E Kiperwasser, Y Goldberg Transactions of the Association for Computational Linguistics 4, 313-327, 2016 | 782 | 2016 |
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models E Ben Zaken, S Ravfogel, Y Goldberg arXiv e-prints, arXiv: 2106.10199, 2021 | 749 | 2021 |
Universal Dependency Annotation for Multilingual Parsing. RT McDonald, J Nivre, Y Quirmbach-Brundage, Y Goldberg, D Das, ... ACL (2), 92-97, 2013 | 707 | 2013 |
Universal Dependency Annotation for Multilingual Parsing. RT McDonald, J Nivre, Y Quirmbach-Brundage, Y Goldberg, D Das, ... ACL (2), 92-97, 2013 | 707 | 2013 |
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them H Gonen, Y Goldberg arXiv preprint arXiv:1903.03862, 2019 | 666 | 2019 |
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks Y Adi, E Kermany, Y Belinkov, O Lavi, Y Goldberg arXiv preprint arXiv:1608.04207, 2016 | 609 | 2016 |
Assessing BERT's Syntactic Abilities Y Goldberg arXiv preprint arXiv:1901.05287, 2019 | 539 | 2019 |
Deep multi-task learning with low level tasks supervised at lower layers A Søgaard, Y Goldberg Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 528 | 2016 |
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss B Plank, A Søgaard, Y Goldberg arXiv preprint arXiv:1604.05529, 2016 | 523 | 2016 |
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? A Jacovi, Y Goldberg arXiv preprint arXiv:2004.03685, 2020 | 517 | 2020 |
A little is enough: Circumventing defenses for distributed learning G Baruch, M Baruch, Y Goldberg Advances in Neural Information Processing Systems 32, 2019 | 439 | 2019 |