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Shun Kiyono
Shun Kiyono
LY Corp.
Verified email at lycorp.co.jp - Homepage
Title
Cited by
Cited by
Year
An empirical study of incorporating pseudo data into grammatical error correction
S Kiyono, J Suzuki, M Mita, T Mizumoto, K Inui
arXiv preprint arXiv:1909.00502, 2019
1622019
ESPnet-ST: All-in-one speech translation toolkit
H Inaguma, S Kiyono, K Duh, S Karita, NEY Soplin, T Hayashi, ...
arXiv preprint arXiv:2004.10234, 2020
1532020
Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction
M Kaneko, M Mita, S Kiyono, J Suzuki, K Inui
arXiv preprint arXiv:2005.00987, 2020
1452020
Lessons on parameter sharing across layers in transformers
S Takase, S Kiyono
arXiv preprint arXiv:2104.06022, 2021
582021
Rethinking perturbations in encoder-decoders for fast training
S Takase, S Kiyono
arXiv preprint arXiv:2104.01853, 2021
402021
Effective adversarial regularization for neural machine translation
M Sato, J Suzuki, S Kiyono
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
352019
Shape: Shifted absolute position embedding for transformers
S Kiyono, S Kobayashi, J Suzuki, K Inui
arXiv preprint arXiv:2109.05644, 2021
282021
Massive exploration of pseudo data for grammatical error correction
S Kiyono, J Suzuki, T Mizumoto, K Inui
IEEE/ACM transactions on audio, speech, and language processing 28, 2134-2145, 2020
182020
Tohoku-AIP-NTT at WMT 2020 news translation task
S Kiyono, T Ito, R Konno, M Morishita, J Suzuki
Proceedings of the Fifth Conference on Machine Translation, 145-155, 2020
152020
A self-refinement strategy for noise reduction in grammatical error correction
M Mita, S Kiyono, M Kaneko, J Suzuki, K Inui
arXiv preprint arXiv:2010.03155, 2020
142020
Pseudo zero pronoun resolution improves zero anaphora resolution
R Konno, S Kiyono, Y Matsubayashi, H Ouchi, K Inui
arXiv preprint arXiv:2104.07425, 2021
112021
Source-side prediction for neural headline generation
S Kiyono, S Takase, J Suzuki, N Okazaki, K Inui, M Nagata
arXiv preprint arXiv:1712.08302, 2017
112017
On layer normalizations and residual connections in transformers
S Takase, S Kiyono, S Kobayashi, J Suzuki
arXiv preprint arXiv:2206.00330, 2022
92022
Mixture of expert/imitator networks: Scalable semi-supervised learning framework
S Kiyono, J Suzuki, K Inui
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4073-4081, 2019
92019
Diverse lottery tickets boost ensemble from a single pretrained model
S Kobayashi, S Kiyono, J Suzuki, K Inui
arXiv preprint arXiv:2205.11833, 2022
82022
An empirical study of contextual data augmentation for japanese zero anaphora resolution
R Konno, Y Matsubayashi, S Kiyono, H Ouchi, R Takahashi, K Inui
arXiv preprint arXiv:2011.00948, 2020
82020
Unsupervised token-wise alignment to improve interpretation of encoder-decoder models
S Kiyono, S Takase, J Suzuki, N Okazaki, K Inui, M Nagata
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and …, 2018
82018
B2t connection: Serving stability and performance in deep transformers
S Takase, S Kiyono, S Kobayashi, J Suzuki
arXiv preprint arXiv:2206.00330, 2022
72022
Lessons on parameter sharing across layers in transformers. CoRR abs/2104.06022 (2021)
S Takase, S Kiyono
arXiv preprint arXiv:2104.06022, 2021
52021
Reducing odd generation from neural headline generation
S Kiyono, S Takase, J Suzuki, N Okazaki, K Inui, M Nagata
Proceedings of the 32nd Pacific Asia Conference on Language, Information and …, 2018
42018
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