| 2025 | ACL | Large Vocabulary Size Improves Large Language Models. | Sho Takase, Ryokan Ri, Shun Kiyono, Takuya Kato |
| 2023 | ACL | B2T Connection: Serving Stability and Performance in Deep Transformers. | Sho Takase, Shun Kiyono, Sosuke Kobayashi, Jun Suzuki |
| 2023 | RANLP | Bridging the Gap between Subword and Character Segmentation in Pretrained Language Models. | Shun Kiyono, Sho Takase, Shengzhe Li, Toshinori Sato |
| 2021 | EMNLP | SHAPE : Shifted Absolute Position Embedding for Transformers. | Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui |
| 2021 | EMNLP | Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution. | Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, Hiroki Ouchi, Kentaro Inui |
| 2021 | NAACL | Rethinking Perturbations in Encoder-Decoders for Fast Training. | Sho Takase, Shun Kiyono |
| 2020 | ACL | ESPnet-ST: All-in-One Speech Translation Toolkit. | Hirofumi Inaguma, Shun Kiyono, Kevin Duh, Shigeki Karita, Nelson Yalta, Tomoki Hayashi, Shinji Watanabe |
| 2020 | ACL | Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction. | Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui |
| 2020 | COLING | An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution. | Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, Kentaro Inui |
| 2020 | EMNLP | A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction. | Masato Mita, Shun Kiyono, Masahiro Kaneko, Jun Suzuki, Kentaro Inui |
| 2019 | AAAI | Mixture of Expert/Imitator Networks: Scalable Semi-Supervised Learning Framework. | Shun Kiyono, Jun Suzuki, Kentaro Inui |
| 2019 | ACL | Effective Adversarial Regularization for Neural Machine Translation. | Motoki Sato, Jun Suzuki, Shun Kiyono |
| 2019 | EMNLP | An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction. | Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, Kentaro Inui |
| 2018 | EMNLP | Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models. | Shun Kiyono, Sho Takase, Jun Suzuki, Naoaki Okazaki, Kentaro Inui, Masaaki Nagata |
| 2018 | PACLIC | Reducing Odd Generation from Neural Headline Generation. | Shun Kiyono, Sho Takase, Jun Suzuki, Naoaki Okazaki, Kentaro Inui, Masaaki Nagata |