Jason Lee

I am a Ph.D student in Computer Science at New York University, where I work on generative modelling with a focus on text. I am a member of the CILVR group and work closely with Kyunghyun Cho.

Before joining NYU, I received my Bachelors and Masters from St John's College, University of Cambridge and worked as a research assistant at ETH Zürich. I also spent time at Facebook AI Research as an intern.

My research is supported by Qualcomm Innovation Fellowship (2016-2017).

Email | CV | Google Scholar | Github



Jason Lee
Papers

Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation
J. Lee, R. Shu and K. Cho
Empirical Methods in Natural Language Processing (EMNLP), 2020
[arxiv] [bib]

On the Discrepancy between Density Estimation and Sequence Generation
J. Lee, D. Tran, O. Firat and K. Cho
Empirical Methods in Natural Language Processing (EMNLP), Workshop on Structured Prediction for NLP, 2020
[arxiv] [bib]

Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic
Inference using a Delta Posterior
R. Shu, J. Lee, H. Nakayama and K. Cho
AAAI Conference on Artificial Intelligence (AAAI) , 2020
[arxiv] [bib]

Multi-Turn Beam Search for Neural Dialogue Modeling
I. Kulikov*, J. Lee* and K. Cho
Neural Information Processing Systems (NeurIPS), Conversational AI Workshop, 2019
[arxiv] [bib]

Countering Language Drift via Visual Grounding
J. Lee, K. Cho and D. Kiela
Empirical Methods in Natural Language Processing (EMNLP), 2019
[arxiv] [bib]

Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
J. Lee*, E. Mansimov* and K. Cho
Empirical Methods in Natural Language Processing (EMNLP), 2018
[arxiv] [code] [bib]

Emergent Translation in Multi-Agent Communication
J. Lee, K. Cho, J. Weston and D. Kiela
International Conference on Learning Representations (ICLR), 2018
[arxiv] [code] [bib]

Fully Character-Level Neural Machine Translation without Explicit Segmentation
J. Lee, K. Cho and T. Hofmann
Transactions of the Association for Computational Linguistics (TACL), 2017
[arxiv] [journal] [code] [bib]