| Enhancing context modeling with a query-guided capsule network for document-level translation Z Yang, J Zhang, F Meng, S Gu, Y Feng, J Zhou arXiv preprint arXiv:1909.00564, 2019 | 71 | 2019 |
| Improving domain adaptation translation with domain invariant and specific information S Gu, Y Feng, Q Liu arXiv preprint arXiv:1904.03879, 2019 | 45 | 2019 |
| Token-level adaptive training for neural machine translation S Gu, J Zhang, F Meng, Y Feng, W Xie, J Zhou, D Yu arXiv preprint arXiv:2010.04380, 2020 | 37 | 2020 |
| Modeling fluency and faithfulness for diverse neural machine translation Y Feng, W Xie, S Gu, C Shao, W Zhang, Z Yang, D Yu Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 59-66, 2020 | 29 | 2020 |
| Pruning-then-expanding model for domain adaptation of neural machine translation S Gu, Y Feng, W Xie arXiv preprint arXiv:2103.13678, 2021 | 27 | 2021 |
| Investigating catastrophic forgetting during continual training for neural machine translation S Gu, Y Feng arXiv preprint arXiv:2011.00678, 2020 | 24 | 2020 |
| Importance-based neuron allocation for multilingual neural machine translation W Xie, Y Feng, S Gu, D Yu arXiv preprint arXiv:2107.06569, 2021 | 22 | 2021 |
| Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappings S Gu, Y Feng arXiv preprint arXiv:2210.15851, 2022 | 20 | 2022 |
| Guiding teacher forcing with seer forcing for neural machine translation Y Feng, S Gu, D Guo, Z Yang, C Shao arXiv preprint arXiv:2106.06751, 2021 | 16 | 2021 |
| Improving multi-head attention with capsule networks S Gu, Y Feng Natural Language Processing and Chinese Computing: 8th CCF International …, 2019 | 14 | 2019 |
| Continual learning of neural machine translation within low forgetting risk regions S Gu, B Hu, Y Feng arXiv preprint arXiv:2211.01542, 2022 | 12 | 2022 |
| Robust neural machine translation with asr errors H Xue, Y Feng, S Gu, W Chen Proceedings of the First Workshop on Automatic Simultaneous Translation, 15-23, 2020 | 11 | 2020 |
| Infinity-mm: Scaling multimodal performance with large-scale and high-quality instruction data S Gu, J Zhang, S Zhou, K Yu, Z Xing, L Wang, Z Cao, J Jia, Z Zhang, ... arXiv preprint arXiv:2410.18558, 2024 | 7 | 2024 |
| Addressing the length bias challenge in document-level neural machine translation Z Zhuocheng, S Gu, M Zhang, Y Feng Findings of the Association for Computational Linguistics: EMNLP 2023, 11545 …, 2023 | 4 | 2023 |
| Enhancing neural machine translation with semantic units L Huang, S Gu, Z Zhang, Y Feng arXiv preprint arXiv:2310.11360, 2023 | 4 | 2023 |
| Scaling law for document neural machine translation Z Zhuocheng, S Gu, M Zhang, Y Feng Findings of the Association for Computational Linguistics: EMNLP 2023, 8290-8303, 2023 | 3 | 2023 |
| Aquila2 technical report BW Zhang, L Wang, J Li, S Gu, X Wu, Z Zhang, B Gao, Y Ao, G Liu arXiv preprint arXiv:2408.07410, 2024 | 2 | 2024 |
| Pruning-then-expanding model for domain adaptation of neural machine translation G Shuhao, Y Feng, W Xie arXiv preprint arXiv 2103, 2021 | 2 | 2021 |
| Improving multilingual neural machine translation by utilizing semantic and linguistic features M Bu, S Gu, Y Feng arXiv preprint arXiv:2408.01394, 2024 | 1 | 2024 |
| CCI3. 0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models L Wang, BW Zhang, C Wu, H Zhao, X Shi, S Gu, J Li, Q Ma, TF Pan, G Liu arXiv preprint arXiv:2410.18505, 2024 | | 2024 |