I'm an applied scientist in Amazon AWS AI Labs working to advance the state-of-the-art of machine translation systems, primarily the Amazon Translate system. Prior to Amazon, I was a PhD student at Department of Computer Science, Johns Hopkins University. My PhD advisor was Philipp Koehn.
During my graduate studies, I was primarily affiliated with Center for Language and Speech Processing and also did research on neural machine translation. Yet, I was also invovled in several projects focused on broader problems of natural language processing (both text and speech). I had also spent a few memorable months either interning at Microsoft Translator, Salesforce Research, Amazon, or visiting The University of Edinburgh.
Before joining Johns Hopkins, I got my Bachelor's degree in Beijing University of Posts & Telecommunications. During the last year of my undergraduate study, I worked with Weiwei Sun in the Language Computing and Web Mining Group of Institute of Computer Science & Technology, Peking University, with a focus on semantic parsing and Chinese word segmentation.
- May 2022: I started to work full-time at Amazon AWS Translate.
- Apr 2022: Defended my doctoral dissertation!
- Aug 2021: Our work on using Levenshtein Transformer for word-level quality estimation will appear in EMNLP 2021.
- Aug 2021: I led the JHU-Microsoft team in the WMT21 word-level quality estimation shared task, where we rank the 1st place on Word-MCC metric for the English-German language pair. The paper describing our method will appear in WMT 2021.
- 600.465: Natural Language Processing
- 600.475: Machine Learning
- 600.468: Machine Translation
- 600.676: Machine Learning: Data to Models
- 050.620: Syntax I
- 600.615: Big Data, Small Languages, Scalable Systems
- 550.661: Nonlinear Optimization I
- 600.420: Parallel Programming
- Nov 2021: Guest Lecture, EN.600.468/601.668 Machine Translation -- Analysis and Visualization
- Fall 2017: Graduate Teaching Assistant, EN.600.468/601.668 Machine Translation. Checkout the neural network and NMT homework I designed.
- Spring 2017: Guest Lecture, EN.600.435 Artificial Intelligence -- Markov Decision Process
- Spring 2016: Guest Lecture, EN.600.468 Machine Translation -- Syntax-Based Models
Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
Weiting Tan, Shuoyang Ding, Huda Khayrallah, Philipp Koehn
The 15th Conference of the Association for Machine Translation in the Americas (AMTA 2022) [pdf]
Runtime Audit of Neural Sequence Models for NLP
PhD Thesis [pdf]
The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task
Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post, Christian Federmann, Philipp Koehn
Sixth Conference on Machine Translation (WMT 2021) [pdf][poster]
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, Sanjeev Khudanpur
ASRU 2019 [pdf][code]
An Exploration of Masking for Neural Machine Translation
Matt Post, Shuoyang Ding, Marianna Martindale and Winston Wu
MT Summit 2019 [pdf]
Grammatical Relations in Chinese: GB-Ground Extraction and Data-Driven Parsing
Weiwei Sun, Yantao Du, Xin Kou, Shuoyang Ding, Xiaojun Wan
Annual Meeting of the Association for Computational Linguistics (ACL) 2014 [pdf][bib]
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?
Shuoyang Ding and Kevin Duh, 2018 [pdf]
Backstitch: Counteracting Finite-sample Bias via Negative Steps
Yiming Wang, Hossein Hadian, Shuoyang Ding, Ke Li, Hainan Xu, Xiaohui Zhang, Daniel Povey, Sanjeev Khudanpur, 2017 [pdf]