Katharina Kann
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Comparative study of cnn and rnn for natural language processing
W Yin, K Kann, M Yu, H Schütze
arXiv preprint arXiv:1702.01923, 2017
3712017
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
822016
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
602018
Single-model encoder-decoder with explicit morphological representation for reinflection
K Kann, H Schütze
arXiv preprint arXiv:1606.00589, 2016
562016
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
372017
One-shot neural cross-lingual transfer for paradigm completion
K Kann, R Cotterell, H Schütze
arXiv preprint arXiv:1704.00052, 2017
292017
The LMU system for the CoNLL-SIGMORPHON 2017 shared task on universal morphological reinflection
K Kann, H Schütze
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal …, 2017
222017
Neural multi-source morphological reinflection
K Kann, R Cotterell, H Schütze
arXiv preprint arXiv:1612.06027, 2016
222016
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
212016
Comparative study of CNN and RNN for natural language processing (2017)
W Yin, K Kann, M Yu, H Schutze
arXiv preprint arXiv:1702.01923, 2017
192017
Comparative study of CNN and RNN for natural language processing. arXiv 2017
W Yin, K Kann, M Yu, H Schütze
arXiv preprint arXiv:1702.01923, 0
15
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
142019
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
K Kann, S Rothe, K Filippova
arXiv preprint arXiv:1809.08731, 2018
142018
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
142018
Verb argument structure alternations in word and sentence embeddings
K Kann, A Warstadt, A Williams, SR Bowman
arXiv preprint arXiv:1811.10773, 2018
132018
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
122018
Exploring cross-lingual transfer of morphological knowledge in sequence-to-sequence models
H Jin, K Kann
Proceedings of the First Workshop on Subword and Character Level Models in …, 2017
82017
Unlabeled data for morphological generation with character-based sequence-to-sequence models
K Kann, H Schütze
arXiv preprint arXiv:1705.06106, 2017
82017
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
52019
Comparative study of CNN and RNN for natural language processing. CoRR abs/1702.01923 (2017)
W Yin, K Kann, M Yu, H Schütze
arXiv preprint arXiv:1702.01923, 2017
52017
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