| Espresso: A fast end-to-end neural speech recognition toolkit Y Wang, T Chen, H Xu, S Ding, H Lv, Y Shao, N Peng, L Xie, S Watanabe, ... 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2019 | 89 | 2019 |
| A Call for Prudent Choice of Subword Merge Operations in Neural Machine Translation S Ding, A Renduchintala, K Duh Proceedings of Machine Translation Summit XVII Volume 1: Research Track, 204-213, 2019 | 84* | 2019 |
| Saliency-driven Word Alignment Interpretation for Neural Machine Translation S Ding, H Xu, P Koehn Proceedings of the Fourth Conference on Machine Translation (WMT) 1, 1-12, 2019 | 71 | 2019 |
| Multi-Modal Data Augmentation for End-to-end ASR A Renduchintala, S Ding, M Wiesner, S Watanabe arXiv preprint arXiv:1803.10299, 2018 | 70 | 2018 |
| Evaluating Saliency Methods for Neural Language Models S Ding, P Koehn arXiv preprint arXiv:2104.05824, 2021 | 62 | 2021 |
| Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling H Xu, S Ding, S Watanabe ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 44 | 2019 |
| The JHU Machine Translation Systems for WMT 2017 S Ding, H Khayrallah, P Koehn, M Post, G Kumar, K Duh Proceedings of the Second Conference on Machine Translation 2, 276–282, 2017 | 25 | 2017 |
| The JHU Machine Translation Systems for WMT 2016 S Ding, K Duh, H Khayrallah, P Koehn, M Post Proceedings of the First Conference on Machine Translation: Volume 2, Shared …, 2016 | 25 | 2016 |
| An Exploration of Placeholding in Neural Machine Translation M Post, S Ding, M Martindale, W Wu Proceedings of Machine Translation Summit XVII Volume 1: Research Track, 182-192, 2019 | 16 | 2019 |
| Levenshtein Training for Word-level Quality Estimation S Ding, M Junczys-Dowmunt, M Post, P Koehn arXiv preprint arXiv:2109.05611, 2021 | 9 | 2021 |
| Grammatical relations in Chinese: GB-ground extraction and data-driven parsing W Sun, Y Du, X Kou, S Ding, X Wan Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 7 | 2014 |
| Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains V Zouhar, S Ding, A Currey, T Badeka, J Wang, B Thompson arXiv preprint arXiv:2402.18747, 2024 | 4 | 2024 |
| The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task S Ding, M Junczys-Dowmunt, M Post, C Federmann, P Koehn https://arxiv.org/abs/2109.08724, 2021 | 4 | 2021 |
| Parallelizable Stack Long Short-Term Memory S Ding, P Koehn Third Workshop on Structured Prediction for NLP, 1-6, 2019 | 4 | 2019 |
| How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation? S Ding, K Duh arXiv preprint arXiv:1806.01515, 2018 | 3 | 2018 |
| EMMeTT: Efficient Multimodal Machine Translation Training P Żelasko, Z Chen, M Wang, D Galvez, O Hrinchuk, S Ding, K Hu, J Balam, ... arXiv preprint arXiv:2409.13523, 2024 | 1 | 2024 |
| Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation W Tan, S Ding, H Khayrallah, P Koehn arXiv preprint arXiv:2110.05691, 2021 | 1 | 2021 |
| RUNTIME AUDIT OF NEURAL SEQUENCE MODELS FOR NLP S Ding Johns Hopkins University, 2022 | | 2022 |
Philipp KoehnProfessor, Johns Hopkins UniversityVerified email at jhu.edu
