| Which coreference evaluation metric do you trust? a proposal for a link-based entity aware metric NS Moosavi, M Strube Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 56 | 2016 |
| Lexical features in coreference resolution: To be used with caution NS Moosavi, M Strube Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 25 | 2017 |
| Anaphora Resolution with the ARRAU Corpus M Poesio, Y Grishina, V Kolhatkar, NS Moosavi, I Roeziger, A Roussel, ... Proceedings of the First Workshop on Computational Models of Reference …, 2018 | 21 | 2018 |
| Tcvisor: A hypervisor level secure storage M Rezaei, NS Moosavi, H Nemati, R Azmi 2010 International Conference for Internet Technology and Secured …, 2010 | 12 | 2010 |
| Coala: A neural coverage-based approach for long answer selection with small data A Rücklé, NS Moosavi, I Gurevych Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 6932-6939, 2019 | 11 | 2019 |
| Using linguistic features to improve the generalization capability of neural coreference resolvers NS Moosavi, M Strube arXiv preprint arXiv:1708.00160, 2017 | 9 | 2017 |
| A ranking approach to Persian pronoun resolution NS Moosavi, G Ghassem-Sani Advances in Computational Linguistics. Research in Computing Science 41, 169-180, 2009 | 9 | 2009 |
| Revisiting Selectional Preferences for Coreference Resolution B Heinzerling, NS Moosavi, M Strube Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 7 | 2017 |
| Search space pruning: A simple solution for better coreference resolvers NS Moosavi, M Strube Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 7 | 2016 |
| Mind the trade-off: Debiasing NLU models without degrading the in-distribution performance PA Utama, NS Moosavi, I Gurevych arXiv preprint arXiv:2005.00315, 2020 | 6 | 2020 |
| Neural duplicate question detection without labeled training data A Rücklé, NS Moosavi, I Gurevych arXiv preprint arXiv:1911.05594, 2019 | 5 | 2019 |
| Unsupervised coreference resolution by utilizing the most informative relations NS Moosavi, M Strube Proceedings of COLING 2014, the 25th International Conference on …, 2014 | 5 | 2014 |
| Improving QA generalization by concurrent modeling of multiple biases M Wu, NS Moosavi, A Rücklé, I Gurevych arXiv preprint arXiv:2010.03338, 2020 | 3 | 2020 |
| Use generalized representations, but do not forget surface features NS Moosavi, M Strube arXiv preprint arXiv:1702.07507, 2017 | 3 | 2017 |
| Towards debiasing NLU models from unknown biases PA Utama, NS Moosavi, I Gurevych arXiv preprint arXiv:2009.12303, 2020 | 2 | 2020 |
| Improving generalization by incorporating coverage in natural language inference NS Moosavi, PA Utama, A Rücklé, I Gurevych arXiv preprint arXiv:1909.08940, 2019 | 2 | 2019 |
| Using automatically extracted minimum spans to disentangle coreference evaluation from boundary detection NS Moosavi, L Born, M Poesio, M Strube arXiv preprint arXiv:1906.06703, 2019 | 2 | 2019 |
| Robustness in Coreference Resolution NS Moosavi | 1 | 2020 |
| Unsupervised Coreference Resolution Using a Graph Labeling Approach NS Moosavi, GR GhassemSani Language and Technology Conference, 93-103, 2011 | 1 | 2011 |
| Coreference Reasoning in Machine Reading Comprehension M Wu, NS Moosavi, D Roth, I Gurevych arXiv preprint arXiv:2012.15573, 2020 | | 2020 |
Michael StrubeHeidelberg Institute for Theoretical Studies gGmbHVerified email at h-its.org
