Yuntian Deng Ph.D. Student Harvard University [dengyuntian@seas.harvard.edu] [cv]

My research aims at generating long-form texts that exhibit high-level plots, conform to domain knowledge, and connect to real-world events while maintaining consistency and continuity. To achieve this goal, my research develops deep generative models, a toolkit that allows us to explicitly model the dependency structures among variables of interest, and learn these dependencies from partially observed data.

I am also interested in open-source projects to make my research efforts more readily available for developers and researchers.

Current Research Area

• Deep generative models for probabilistic text generation.

Selected Papers

	Cascaded Text Generation with Markov Transformers Yuntian Deng, Alexander M. Rush. NeurIPS 2020
	Residual Energy-Based Models for Text Generation Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, Marc'Aurelio Ranzato. ICLR 2020
	Latent Alignment and Variational Attention Yuntian Deng*, Yoon Kim*, Justin Chiu, Demi Guo, Alexander M. Rush. NIPS 2018
	Image-to-Markup Generation with Coarse-to-Fine Attention Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M. Rush. ICML 2017
	Neural Linguistic Steganography Zachary Ziegler*, Yuntian Deng*, Alexander Rush. EMNLP 2019 (oral)
	OpenNMT: Open-Source Toolkit for Neural Machine Translation Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush. ACL Demo 2017 (Best Demo Runner-up)

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