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Alexander RushAssociate Professor.Cornell University CS+Cornell Tech (NYC), Cornell NLP [arush@cornell.edu] [cv] Researcher. Hugging Face |
My research aims to build NLP systems that are safe, fast, and controllable. We are interested primarily in tasks that involve text generation such as machine translation, document summarization, and data-to-text generation. Methodologically, we study data-driven probabilistic methods that combine deep-learning based models with probabilistic controls.
I am also interested in open-source NLP and deep learning, and develop projects to make deep learning systems safer, more clear, and easier to use. I work part-time at Hugging Face and like to release various software projects to support NLP and DL research.
Current Research Areas
- Interpretable and controllable natural language generation for data-to-text summary.
- Deep generative models for probabilistic text processing and understanding.
- Efficient algorithms and hardware for speech, translation, and dialogue.
- Visual tools for understanding of neural language models.
Recognition
My group's work has been recognized with an NSF CAREER Award and a Sloan Fellowship. We have won paper awards at conferences for NLP, Hardware, and Visualization, as well as awards for best demonstrations for open-source software.
Selected Papers
A selection of papers from the last five years that represent my research interests and style.
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, et al.. ICLR 2022 |
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How many data points is a prompt worth?
Teven Le Scao, Alexander M. Rush. NAACL Short 2021 |
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Transformers: State-of-the-art Natural Language Processing
Thomas Wolf et al. EMNLP Demos 2020 |
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Compound Probabilistic Context-Free Grammars for Grammar Induction
Yoon Kim, Chris Dyer, Alexander M. Rush. ACL 2019 |
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Learning Neural Templates for Text Generation
Sam Wiseman, Stuart M. Shieber, Alexander Rush. EMNLP 2018 |
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LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M. Rush. InfoVis 2017 |
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OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush. ACL Demo 2017 |
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Sequence-Level Knowledge Distillation
Yoon Kim and Alexander M. Rush. EMNLP 2016 |
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Character-Aware Neural Language Models
Yoon Kim, Yacine Jernite, David Sontag, and Alexander M. Rush. AAAI 2016 |
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A Neural Attention Model for Abstractive Sentence Summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. EMNLP 2015. |