PhD at Max Planck Institute & ETH, affiliated at UMich (2020-2024 expected)
Email: jinzhi@umich.edu (This is a Gmail account)
👩🎓Google Scholar / 📁CV / 
@ZhijingJin
I am an enthusiastic AI researcher and social good promoter. I am currently a 2nd-year PhD (started in 2020) supervised by Prof Bernhard Schoelkopf at Max Planck Institute & ETH, mentored by Prof Rada Mihalcea (UMich), and co-supervised through the ELLIS PhD Program by Prof Mrinmaya Sachan and Prof Ryan Cotterell at ETH.
My research has two goals: (1) to promote NLP for social good, and (2) to improve AI by connecting NLP with causality. And my career goal is to be a professor in NLP. (Quick pointers: my bio, mentors & mentees, and publications.)
Highlights
- If you are a master/undergrad student looking for research experience, feel free to read the eligibility in this application form and fill it out. My projects recently need a student or collaborator who is highly familiar with Twitter (e.g., what gets popular; what gets banned; what are controversial social topics), and technical background is not compulsory. Feel free to directly email me if you are a fit.
- For my NLP for Social Good line of work (5+ papers, 3 workshops):
- Check out my position papers “How Good Is NLP?” (ACL 2021 Findings), NLP Alignment for AI safety (2021).
- Check out my research on NLP for COVID policies (EMNLP 2021 Findings), for misinformation in climate claims (arXiv 2022), for healthcare (AAHPM 2020, JPSM 2020), for political analysis (book chapter, 2021), with an overview here.
- For my Causality+NLP line of work (6+ papers, 3 workshop/conference/tutorial):
- Check out (1) my new arXiv (logical fallacy detection),
- (2) causal insights to improve NLP modeling (causal direction of data collection matters, EMNLP 2021 oral),
- (3) causal methods to analyze linguistic phenomena (Slangvolution, ACL 2022) and policies (COVID policies, EMNLP 2021 Findings).
- Upcoming: Stay tuned for my CausalNLP tutorial at EMNLP 2022.
- For my efforts to establish a global supportive network for NLP researchers: Check out our ACL Year-Round Mentorship (a network of 650+ mentees and 90+ NLP mentors), my list of useful resources for career advice in NLP/AI, and other resources.
Publications
To follow talks on my research, please check out my YouTube. For the .bib file of my publications, please check here.
Logical Fallacy Detection
Zhijing Jin*, Abhinav Lalwani*, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf
arXiv 2022 / Paper / Code
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
Zhijing Jin, Geeticka Chauhan, Brian Tse, Mrinmaya Sachan, Rada Mihalcea
Findings of ACL 2021 / Paper / Code / Poster / Slides / Talk
Invited Talk at CyberValley, Germany
TextGen Deep Learning for Text Style Transfer: A Survey
Di Jin*, Zhijing Jin*, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
CL Journal 2022 / Paper / Slides
Robust Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
Xiaoyu Xing*, Zhijing Jin*, Di Jin, Bingning Wang, Qi Zhang, Xuanjing Huang
EMNLP 2020 / Paper / Code / Slides
TextGen IE Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf
AISTATS 2021 / Paper / Poster / Code
TextGen IE GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for
Unsupervised Graph-to-Text Generation
Zhijing Jin*, Qipeng Guo*, Xipeng Qiu, Zheng Zhang
COLING 2020 / Paper / Poster / Code
TextGen P2: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Qipeng Guo, Zhijing Jin, Ning Dai, Xipeng Qiu, Xiangyang Xue, David Wipf, Zheng Zhang
INLG 2020 Workshop / Paper / Code
Top #1 in the Leaderboard of WebNLG 2020
TextGen IE CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Qipeng Guo*, Zhijing Jin*, Xipeng Qiu, Weinan Zhang, David Wipf, Zheng Zhang
INLG 2020 Workshop (Oral) / Paper / Code
IE Relation of the Relations: A New Paradigm of the Relation Extraction Problem
Zhijing Jin*, Yongyi Yang*, Xipeng Qiu, Zheng Zhang
EMNLP 2020 (later withdrawn) / Paper / Code
TextGen A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
Di Jin, Zhijing Jin, Tianyi Joey Zhou, Peter Szolovits
arXiv 2020 / Paper / Code
Robust Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Di Jin*, Zhijing Jin*, Tianyi Joey Zhou, Peter Szolovits
AAAI 2020 (20.6%) / Paper / Code / Slides / Poster / MIT News / ACM TechNews / WeVolver / VentureBeat / Synced (Chinese)
Oral Presentation
Medical Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients with Heart Failure Undergoing Cardiac Resynchronization Therapy (CRT)
Richard Leiter*, Enrico Santus*, Zhijing Jin, Katherine Lee, …, Charlotta Lindvall
AAHPM 2020 / Paper / Code
TextGen IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Zhijing Jin*, Di Jin*, Jonas Mueller, Nicholas Matthews, Enrico Santus
EMNLP 2019 / Paper / Code / Poster
IE GraphIE: A Graph-Based Framework for Information Extraction
Yujie Qian, Enrico Santos, Zhijing Jin, Jiang Guo, Regina Barzilay
NAACL 2019 / Paper / Code
Tool 3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Zhijing Jin, Tristan Swedish, Ramesh Raskar
arXiv 2018 / Paper / Code
CogSci Prediction of Story Ending with Optimistic and Pessimistic Mindsets
Zhijing Jin, Prof. Patrick Winston (1943-2019)
Tool Word Representations for Computing Semantic Relatedness
Zhijing Jin, Xiaolong Gong, Linpeng Huang
In contribution to the AAAI 2018 paper.
Tutorial & Survey
NLP Graph Neural Net Applications for Natural Language Processing
Xipeng Qiu, Zhijing Jin, Xiangkun Hu
Paper 2020