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PhD at Max Planck Institute & ETH Zürich (2020-)
📭 Email: zhijing.jin@connect.hku.hk (If there are admin tasks I forget to reply to, please contact my secretary at secretary.of.zhijing@gmail.com)
👩‍🎓Google Scholar / 📁CV / twitter-icon@ZhijingJin

I am an enthusiastic AI researcher and promoter for social good. I am a Ph.D. (started in 2020) working on two goals: (1) to expand the impact of NLP by promoting NLP for social good, and (2) to improve AI by connecting NLP with causal inference. I am supervised by Bernhard Schoelkopf at Max Planck Institute for Intelligent Systems, Germany & ETH Zürich. I am mentored by Prof Rada Mihalcea at University of Michigan, and we promote NLP for social good and ACL Year-Round Mentorship. I am also an ELLIS PhD (<5% acceptance rate) at MPI and ETH, and my ETH co-supervisors are Prof Mrinmaya Sachan and Prof Ryan Cotterell. Previously, I was a research intern at Amazon AI (2019-2020), supervised by Prof Zheng Zhang, Prof Xipeng Qiu and Dr. David Wipf. Previously, I obtained my bachelor’s degree (1st Class Honors) from the University of Hong Kong with my bachelor thesis supervised by Prof Benjamin Kao. During undergrad, I had visiting semesters at MIT and National Taiwan University. [Here is my bio for talks]

My career goal is to be a professor in NLP. My commitments are

1) Promote NLP for Social Good: See our NLP4SG initiative. Check out our position paper “How Good Is NLP?” at ACL 2021 Findings, my curated list of NLP4SG papers, and our slack. I organize the yearly workshop of NLP for Positive Impact Workshop (e.g., at ACL 2021), and I have organized the RobustML Workshop at ICLR 2021. I am also engaged in Effective Altruism, a social movement to involve people in doing good with high efficiency.
Papers: How Good Is NLP? (position paper at ACL 2021 Findings), NLP to analyze COVID policies (EMNLP 2021 Findings), NLP for healthcare (AAHPM 2020, JPSM 2020), NLP for climate change (Ongoing).
2) Connect NLP & Causality: I am the Publications Chair for the 1st conference on Causal Learning and Reasoning (CLeaR).
Papers: Causality within NLP tasks (Independent Causal Mechanism for NLP, translationese – ongoing), causality for decision-making (policy analysis, career coaching – ongoing).
3) Establish a global supportive network for NLP researchers: I co-organize the ACL Year-Round Mentorship program. I also compile a list of useful resources for career advice from undergrad, PhD to professorships in NLP/AI.


Before starting my PhD (from undergrad to 2021), I spent huge efforts to get familiarized with many building blocks of NLP, including
Text Generation TextGen (6 papers on Graph-to-text generation; 3 papers on text style transfer (EMNLP 2019, ACL 2020, CL Journal 2021 survey); 2 papers on adversarial text generation (for NLP robustness Robust; AAAI 2020 oral, EMNLP 2020).
Information Extraction IE (3 papers on Relation Extraction (RE) (EMNLP 2020, AISTATS 2021, COLING 2020), 3 papers on Named Entity Recognition (NER) (NAACL 2019, Medical JPSM 2020, AAHPM 2020)).

I also have a growing interest in epistemology (the science of science and all things we do), AI for decision-making (including policy making, organizational design, and life coaching; I really like this newsletter). Feel free to contact me for interdisciplinary research.


[For research mentees] I usually supervise Master students at ETH Zürich, for at least 6-month full-time research. In very special cases, I can (remotely) mentor other research mentees with strong coding skills, and longer-than-4-month full-time commitment. If you are interested in working on NLP with me, feel free to fill in this application form, and I will contact you if we can have you remotely collaborate with ETH or UMich labs. It is unpaid w.r.t. money, but fruitful w.r.t. research experience and fun :).

[Apply to our ACL Year-Round Mentorship Program] We are supporting junior researchers in NLP. You can apply as a mentee, mentor, and volunteer. By this program, you can have monthly Q&A sessions with senior NLP profs/researchers, and join a slack channel to get to know world-wide NLP researchers. I compiled a GitHub list of resources to help Global Equality for PhDs in NLP.

Organized Events & Talks

News

Publications

For the .bib file of my publications, please check here.

Independent Causal Mechanisms for NLP

Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning in NLP
Zhijing Jin*, Julius von Kügelgen*, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, Bernhard Schoelkopf
EMNLP 2021 (Oral) / Paper / Code

Causation Between COVID Twitter Sentiments and Policies

Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States
Zhijing Jin, Zeyu Peng, Tejas Vaidhya, Bernhard Schoelkopf, Rada Mihalcea
Findings of EMNLP 2021 / 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

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

TextGen Deep Learning for Text Style Transfer: A Survey
Di Jin*, Zhijing Jin*, Zhiting Hu, Olga Vechtomova, Rada Mihalcea
arXiv 2020 / Paper

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 Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Di Jin, Zhijing Jin, Tianyi Joey Zhou, Lisa Orii, Peter Szolovits
ACL 2020 / Paper / Code / Slides / Talk

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 An Artificial Intelligence Algorithm to Identify Documented Symptoms in Patients with Heart Failure who Received Cardiac Resynchronization Therapy
Richard Leiter, Enrico Santus, Zhijing Jin, Katherine Lee, …, Charlotta Lindvall
JPSM 2020 / Article / Code

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