Assistant Professor of Computer Science - Scholar in Residence 2020-21, joining Duke in Fall 2021
- swiseman at ttic.edu
- Web page:
Ph.D., Harvard University, 2018
A.B., Princeton University, 2010
Sam Wiseman's research focuses on natural language processing. He is broadly interested in deep learning approaches to structured prediction for natural language processing problems, with a particular recent interest in structured approaches to text generation.
- ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation. Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, and Kevin Gimpel. ACL, 2020.
- Amortized Bethe Free Energy Minimization for Learning MRFs. Sam Wiseman and Yoon Kim. NeurIPS, 2019.
- Label-Agnostic Sequence Labeling by Copying Nearest Neighbors. Sam Wiseman and Karl Stratos. ACL, 2019.
- A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations. Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. NAACL, 2019.
- Learning Neural Templates for Text Generation. Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. EMNLP, 2018.