| Comparative study of CNN and RNN for natural language processing W Yin, K Kann, M Yu, H Schütze arXiv preprint arXiv:1702.01923, 2017 | 1152 | 2017 |
| Intermediate-task transfer learning with pretrained models for natural language understanding: When and why does it work? Y Pruksachatkun, J Phang, H Liu, PM Htut, X Zhang, RY Pang, C Vania, ... arXiv preprint arXiv:2005.00628, 2020 | 226 | 2020 |
| The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection R Cotterell, C Kirov, J Sylak-Glassman, G Walther, E Vylomova, ... arXiv preprint arXiv:1810.07125, 2018 | 138 | 2018 |
| MED: The LMU system for the SIGMORPHON 2016 shared task on morphological reinflection K Kann, H Schütze Proceedings of the 14th SIGMORPHON Workshop on Computational Research in …, 2016 | 109 | 2016 |
| Single-model encoder-decoder with explicit morphological representation for reinflection K Kann, H Schütze arXiv preprint arXiv:1606.00589, 2016 | 90 | 2016 |
| Fortification of neural morphological segmentation models for polysynthetic minimal-resource languages K Kann, M Mager, I Meza-Ruiz, H Schütze arXiv preprint arXiv:1804.06024, 2018 | 64 | 2018 |
| English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too J Phang, PM Htut, Y Pruksachatkun, H Liu, C Vania, K Kann, I Calixto, ... AACL 2020, 2020 | 61 | 2020 |
| Training data augmentation for low-resource morphological inflection T Bergmanis, K Kann, H Schütze, S Goldwater Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal …, 2017 | 60 | 2017 |
| Sentence-level fluency evaluation: References help, but can be spared! K Kann, S Rothe, K Filippova arXiv preprint arXiv:1809.08731, 2018 | 58 | 2018 |
| Findings of the AmericasNLP 2021 shared task on open machine translation for indigenous languages of the Americas M Mager, A Oncevay, A Ebrahimi, J Ortega, AR Gonzales, A Fan, ... Proceedings of the First Workshop on Natural Language Processing for …, 2021 | 52 | 2021 |
| jiant 1.2: A software toolkit for research on general-purpose text understanding models A Wang, IF Tenney, Y Pruksachatkun, K Yu, J Hula, P Xia, R Pappagari, ... Note: http://jiant. info/Cited by: footnote 4, 2019 | 50 | 2019 |
| Neural morphological analysis: Encoding-decoding canonical segments K Kann, R Cotterell, H Schütze Proceedings of the 2016 conference on empirical methods in natural language …, 2016 | 47 | 2016 |
| Americasnli: Evaluating zero-shot natural language understanding of pretrained multilingual models in truly low-resource languages A Ebrahimi, M Mager, A Oncevay, V Chaudhary, L Chiruzzo, A Fan, ... arXiv preprint arXiv:2104.08726, 2021 | 46 | 2021 |
| Towards realistic practices in low-resource natural language processing: The development set K Kann, K Cho, SR Bowman arXiv preprint arXiv:1909.01522, 2019 | 45 | 2019 |
| One-shot neural cross-lingual transfer for paradigm completion K Kann, R Cotterell, H Schütze arXiv preprint arXiv:1704.00052, 2017 | 42 | 2017 |
| Verb argument structure alternations in word and sentence embeddings K Kann, A Warstadt, A Williams, SR Bowman arXiv preprint arXiv:1811.10773, 2018 | 40 | 2018 |
| Neural multi-source morphological reinflection K Kann, R Cotterell, H Schütze arXiv preprint arXiv:1612.06027, 2016 | 37 | 2016 |
| Probing for semantic classes: Diagnosing the meaning content of word embeddings Y Yaghoobzadeh, K Kann, TJ Hazen, E Agirre, H Schütze arXiv preprint arXiv:1906.03608, 2019 | 35 | 2019 |
| Comparative study of CNN and RNN for natural language processing (2017) W Yin, K Kann, M Yu, H Schütze arXiv preprint arXiv:1702.01923, 2017 | 35 | 2017 |
| Character-level supervision for low-resource POS tagging K Kann, J Bjerva, I Augenstein, B Plank, A Søgaard Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP …, 2018 | 31 | 2018 |
Hinrich SchützeUniversity of MunichVerified email at cis.lmu.de
