Jiangjie Chen (陈江捷) is a fourth-year Ph.D. candidate at Fudan University in School of Computer Science, Shanghai, China, where he is advised by Prof. Yanghua Xiao at Knowledge Works Lab. He is also currently a research intern at ByteDance AI Lab and UCSB.
He is devoted to reasoning over natural language and making machines being right for the right reasons. His main interested research topics include (but not limited to):
His previous research experiences also include Knowledge Acquisition.
( Download my resumé. Could be outdated. 😶)
Ph.D. in CS, 2019 - 2024 (estimated)
B.S. in CS (with honor), 2014 - 2019
Nov. 2022: Two papers accepted to AAAI 2023! One is VENCE on correcting factual errors in texts, and the other is NEON on explaining why a statement is false: both focus on solving the tasks without direct supervision. Welcome to check it out!
Oct. 2022: Gave a talk at MSRA.
Oct. 2022: I was awarded with China National Scholarship for Doctoral Students.
Sept. 2022: Just married💕!
Sept. 2022: We officially release a new version of the E-KAR dataset (v1.0 -> v1.1), with a substantially improved English dataset! Over 600 problems and 1,000 explanation texts are manually adjusted, and we are as strict as we can! See more information at the E-KAR project page. Have fun!
July 2022: Talk@将门创投 titled “Right for the Right Reasons: Explainable Reasoning on Analogical Recognition and Fact Verification” (in Chinese).
July 2022: ACT for NAT will be presented at NAACL-HLT 2022.
May 2022: E-KAR will be presented at the Commonsense Representation and Reasoning (CSRR) workshop at ACL 2022, discussions welcomed!
May 2022: E-KAR will be presented at ACL 2022 (virtually) in a poster session, welcome to check it out!
Apr. 2022: Our paper (ACT) on non-autoregressive translation got accepted at NAACL-HLT 2022!
Feb. 2022: Our paper (E-KAR) on analogical reasoning got accepted at ACL 2022 (Findings)!
Fact verification guided iterative factual error correction without the supervision from corretion.
We benchmark knowledge-intensive analogical reasoning with human-annotated explanations.
Interpretable fact verification with phrasal decomposition and logic regularization.