Katharina Kann
TitleCited byYear
Comparative study of CNN and RNN for natural language processing
W Yin, K Kann, M Yu, H Schütze
arXiv preprint arXiv:1702.01923, 2017
1972017
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
512016
Single-model encoder-decoder with explicit morphological representation for reinflection
K Kann, H Schütze
arXiv preprint arXiv:1606.00589, 2016
372016
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
16*2018
Neural multi-source morphological reinflection
K Kann, R Cotterell, H Schütze
arXiv preprint arXiv:1612.06027, 2016
152016
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
132017
One-shot neural cross-lingual transfer for paradigm completion
K Kann, R Cotterell, H Schütze
arXiv preprint arXiv:1704.00052, 2017
132017
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
112017
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
102018
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
102016
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
10
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
72017
Unlabeled Data for Morphological Generation With Character-Based Sequence-to-Sequence Models
K Kann, H Schütze
arXiv preprint arXiv:1705.06106, 2017
52017
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
K Kann, S Rothe, K Filippova
arXiv preprint arXiv:1809.08731, 2018
42018
Verb argument structure alternations in word and sentence embeddings
K Kann, A Warstadt, A Williams, SR Bowman
arXiv preprint arXiv:1811.10773, 2018
32018
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
32017
The NYU System for the CoNLL–SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection
K Kann, S Lauly, K Cho
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal …, 2018
12018
Evaluating word embeddings in multi-label classification using fine-grained name typing
Y Yaghoobzadeh, K Kann, H Schütze
arXiv preprint arXiv:1807.07186, 2018
12018
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
12018
Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
M Mager, E Mager, A Medina-Urrea, I Meza, K Kann
arXiv preprint arXiv:1807.00286, 2018
12018
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