| Active learning for BERT: an empirical study LE Dor, A Halfon, A Gera, E Shnarch, L Dankin, L Choshen, M Danilevsky, ... Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 254 | 2020 |
| An autonomous debating system N Slonim, Y Bilu, C Alzate, R Bar-Haim, B Bogin, F Bonin, L Choshen, ... Nature 591 (7850), 379-384, 2021 | 248 | 2021 |
| Semantic inference at the lexical-syntactic level R Bar-Haim, I Dagan, I Greental, E Shnarch Proceedings of the National Conference on Artificial Intelligence 22 (1), 871, 2007 | 91 | 2007 |
| Are you convinced? choosing the more convincing evidence with a Siamese network M Gleize, E Shnarch, L Choshen, L Dankin, G Moshkowich, R Aharonov, ... arXiv preprint arXiv:1907.08971, 2019 | 90 | 2019 |
| Instance-based evaluation of entailment rule acquisition I Szpektor, E Shnarch, I Dagan Proceedings of the 45th Annual Meeting of the Association of Computational …, 2007 | 85 | 2007 |
| Will it blend? blending weak and strong labeled data in a neural network for argumentation mining E Shnarch, C Alzate, L Dankin, M Gleize, Y Hou, L Choshen, R Aharonov, ... Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 84 | 2018 |
| Corpus wide argument mining—a working solution L Ein-Dor, E Shnarch, L Dankin, A Halfon, B Sznajder, A Gera, C Alzate, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7683-7691, 2020 | 81 | 2020 |
| Zero-shot text classification with self-training A Gera, A Halfon, E Shnarch, Y Perlitz, L Ein-Dor, N Slonim arXiv preprint arXiv:2210.17541, 2022 | 60 | 2022 |
| Text categorization from category name via lexical reference L Barak, I Dagan, E Shnarch Proceedings of Human Language Technologies: The 2009 Annual Conference of …, 2009 | 46 | 2009 |
| Extracting lexical reference rules from Wikipedia E Shnarch, L Barak, I Dagan Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, 2009 | 43 | 2009 |
| Efficient Semantic Deduction and Approximate Matching over Compact Parse Forests. R Bar-Haim, I Dagan, S Mirkin, E Shnarch, I Szpektor, J Berant, I Greental TAC, 2008 | 42 | 2008 |
| Evaluating the inferential utility of lexical-semantic resources S Mirkin, I Dagan, E Shnarch Proceedings of the 12th Conference of the European Chapter of the ACL (EACL …, 2009 | 39 | 2009 |
| Efficient benchmarking (of language models) Y Perlitz, E Bandel, A Gera, O Arviv, L Ein-Dor, E Shnarch, N Slonim, ... arXiv preprint arXiv:2308.11696, 2023 | 27 | 2023 |
| Cluster & tune: Boost cold start performance in text classification E Shnarch, A Gera, A Halfon, L Dankin, L Choshen, R Aharonov, ... arXiv preprint arXiv:2203.10581, 2022 | 27 | 2022 |
| Lexical reference: a semantic matching subtask O Glickman, E Shnarch, I Dagan Proceedings of the 2006 Conference on Empirical Methods in Natural Language …, 2006 | 23 | 2006 |
| Addressing Discourse and Document Structure in the RTE Search Task. S Mirkin, R Bar-Haim, I Dagan, E Shnarch, A Stern, I Szpektor, J Berant TAC, 2009 | 22 | 2009 |
| Label sleuth: From unlabeled text to a classifier in a few hours E Shnarch, A Halfon, A Gera, M Danilevsky, Y Katsis, L Choshen, ... arXiv preprint arXiv:2208.01483, 2022 | 21 | 2022 |
| A probabilistic modeling framework for lexical entailment E Shnarch, J Goldberger, I Dagan Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011 | 18 | 2011 |
| Genie: Achieving human parity in content-grounded datasets generation A Yehudai, B Carmeli, Y Mass, O Arviv, N Mills, A Toledo, E Shnarch, ... arXiv preprint arXiv:2401.14367, 2024 | 17 | 2024 |
| The benefits of bad advice: Autocontrastive decoding across model layers A Gera, R Friedman, O Arviv, C Gunasekara, B Sznajder, N Slonim, ... arXiv preprint arXiv:2305.01628, 2023 | 16 | 2023 |