Yuhao Zhang

Hey there, welcome!

I am currently an NLP scientist at Amazon AWS AI, on a mission to deliver innovative and state-of-the-art AI/NLP services to enterprises and end users.

Before Amazon, I obtained my PhD degree from Stanford University, where I was jointly advised by Prof. Chris Manning in the Stanford NLP Group and Prof. Curtis Langlotz in the Stanford AIMI Center. My PhD work has focused on natural language processing and its applications in medicine.

Before that, I obtained a M.S. degree in the Computer Science Department at Stanford University, and a bachelor’s degree from the Department of Electronic Engineering at Tsinghua University, China.

research interest

I care about NLP systems and their impact in real-world applications. My research work has focused on four key areas:

information extraction, with a focus on understanding entities and relations in both general newswire and biomedical text;
text summarization, with a focus on improving the factual correctness of summarization systems;
multimodal learning, with a focus on joint modeling of medical images and text;
syntactic analysis and open-source NLP toolkit, and I am a co-author of the widely used Stanza NLP library.

contact

You can reach me now at {first-name} ~at~ cs.stanford.edu. You can also find my various social accounts at the bottom of this page.

selected publications

For a complete list, see the publications page, or my google scholar page.

(*=equal contribution)

arXiv
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Yang Wang, and Zhiheng Huang

arXiv preprint arXiv:2212.08780, 2022.

Bib HTML
@article{chen2022improving, title = {Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations}, author = {Chen, Jifan and Zhang, Yuhao and Liu, Lan and Dong, Rui and Chen, Xinchi and Ng, Patrick and Wang, William Yang and Huang, Zhiheng}, journal = {arXiv preprint arXiv:2212.08780}, year = {2022}, html = {https://arxiv.org/abs/2212.08780}, abbr = {arXiv}, bibtex_show = {true}, selected = {true} }
arXiv
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks

Kaiser Sun, Peng Qi, Yuhao Zhang, Lan Liu, William Yang Wang, and Zhiheng Huang

arXiv preprint arXiv:2212.09912, 2022.

Bib HTML
@article{sun2022tokenization, title = {Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks}, author = {Sun, Kaiser and Qi, Peng and Zhang, Yuhao and Liu, Lan and Wang, William Yang and Huang, Zhiheng}, journal = {arXiv preprint arXiv:2212.09912}, year = {2022}, html = {https://arxiv.org/abs/2212.09912}, abbr = {arXiv}, bibtex_show = {true}, selected = {true} }
MLHC
Contrastive Learning of Medical Visual Representations from Paired Images and Text

Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz

In Proceedings of the 7th Machine Learning for Healthcare Conference, 2022.

Bib HTML Dataset
@inproceedings{zhang2022contrastive, title = {Contrastive Learning of Medical Visual Representations from Paired Images and Text}, author = {Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D and Langlotz, Curtis P}, booktitle = {Proceedings of the 7th Machine Learning for Healthcare Conference}, pages = {1--24}, volume = {182}, year = {2022}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR}, html = {https://arxiv.org/abs/2010.00747}, dataset = {https://github.com/yuhaozhang/convirt}, abbr = {MLHC}, selected = {true}, bibtex_show = {true} }

Thesis

Deep Understanding and Generation of Medical Text and Beyond

Yuhao Zhang

Stanford University PhD Thesis, 2021.

Bib HTML

@article{zhang2021deep,
  title = {Deep Understanding and Generation of Medical Text and Beyond},
  author = {Zhang, Yuhao},
  year = {2021},
  journal = {Stanford University PhD Thesis},
  school = {Stanford University},
  html = {https://searchworks.stanford.edu/view/13826064},
  abbr = {Thesis},
  bibtex_show = {true},
  selected = {true}
}

JAMIA
Biomedical and Clinical English Model Packages for the Stanza Python NLP Library

Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D Manning, and Curtis P. Langlotz

Journal of the American Medical Informatics Association, 2021.

Bib HTML
@article{zhang2021biomedical, title = {Biomedical and Clinical English Model Packages for the Stanza Python NLP Library}, author = {Zhang, Yuhao and Zhang, Yuhui and Qi, Peng and Manning, Christopher D and Langlotz, Curtis P.}, journal = {Journal of the American Medical Informatics Association}, volume = {28}, number = {9}, pages = {1892--1899}, year = {2021}, publisher = {Oxford University Press}, html = {https://academic.oup.com/jamia/article/28/9/1892/6307885}, abbr = {JAMIA}, bibtex_show = {true}, selected = {true} }
NAACL
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky

In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021.

Bib HTML
@inproceedings{miura2021improving, title = {Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation}, author = {Miura, Yasuhide and Zhang, Yuhao and Tsai, Emily and Langlotz, Curtis and Jurafsky, Dan}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2021}, html = {https://arxiv.org/abs/2010.10042}, abbr = {NAACL}, selected = {true}, bibtex_show = {true} }
EACL
Do Syntax Trees Help Pre-trained Transformers Extract Information?

Devendra Sachan, Yuhao Zhang, Peng Qi, and William L Hamilton

In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021.

Bib HTML
@inproceedings{sachan2021syntax, title = {Do Syntax Trees Help Pre-trained Transformers Extract Information?}, author = {Sachan, Devendra and Zhang, Yuhao and Qi, Peng and Hamilton, William L}, booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL)}, year = {2021}, html = {https://arxiv.org/abs/2008.09084}, abbr = {EACL}, selected = {true}, bibtex_show = {true} }
ACL
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Peng Qi*, Yuhao Zhang*, Yuhui Zhang, Jason Bolton, and Christopher D Manning

In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations, 2020.

Bib HTML
@inproceedings{qi2020stanza, title = {Stanza: A Python Natural Language Processing Toolkit for Many Human Languages}, author = {Qi*, Peng and Zhang*, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL): System Demonstrations}, year = {2020}, abbr = {ACL}, html = {https://stanfordnlp.github.io/stanza/}, selected = {true}, bibtex_show = {true} }
ACL
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports

Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D Manning, and Curtis Langlotz

In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020.

Bib HTML
@inproceedings{zhang2020optimizing, title = {Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports}, author = {Zhang, Yuhao and Merck, Derek and Tsai, Emily and Manning, Christopher D and Langlotz, Curtis}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)}, year = {2020}, html = {https://arxiv.org/abs/1911.02541}, abbr = {ACL}, selected = {true}, bibtex_show = {true} }
EMNLP
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

Yuhao Zhang*, Peng Qi*, and Christopher D Manning

In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.

Bib HTML
@inproceedings{zhang2018graph, title = {Graph Convolution over Pruned Dependency Trees Improves Relation Extraction}, author = {Zhang*, Yuhao and Qi*, Peng and Manning, Christopher D}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2018}, html = {https://aclanthology.org/D18-1244/}, abbr = {EMNLP}, selected = {true}, bibtex_show = {true} }
EMNLP-CoNLL
Universal Dependency Parsing from Scratch

Peng Qi*, Timothy Dozat*, Yuhao Zhang*, and Christopher D Manning

In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, 2018.

Bib HTML
@inproceedings{qi2018universal, title = {Universal Dependency Parsing from Scratch}, author = {Qi*, Peng and Dozat*, Timothy and Zhang*, Yuhao and Manning, Christopher D}, booktitle = {Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}, year = {2018}, html = {https://arxiv.org/abs/1901.10457}, abbr = {EMNLP-CoNLL}, selected = {true}, bibtex_show = {true} }
EMNLP
Position-aware Attention and Supervised Data Improve Slot Filling

Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D Manning

In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.

Bib HTML Dataset
@inproceedings{zhang2017position, title = {Position-aware Attention and Supervised Data Improve Slot Filling}, author = {Zhang, Yuhao and Zhong, Victor and Chen, Danqi and Angeli, Gabor and Manning, Christopher D}, booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2017}, html = {https://aclanthology.org/D17-1004/}, dataset = {https://nlp.stanford.edu/projects/tacred/}, abbr = {EMNLP}, selected = {true}, bibtex_show = {true} }