Research
QANTA investigates question answering at the intersection of natural language processing, human–computer interaction, and machine learning. Our work spans dataset creation, system building, and competition design.
Areas
Projects
Individual research efforts with papers, code, and data — from adversarial question writing to graph-based QA systems.
Data & Code
Quiz bowl corpora, specialized QA benchmarks, starter systems, and download links in one place.
Selected Publications
| Paper | Authors | Venue |
|---|---|---|
| Trick Me If You Can: Human-in-the-loop Generation of Adversarial Examples for Question Answering | Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, Jordan Boyd-Graber | TACL 2019 |
| Dataset and Baselines for Sequential Open-Domain Question Answering | Ahmed Elgohary, Chen Zhao, Jordan Boyd-Graber | EMNLP 2018 |
| Can You Unpack That? Learning to Rewrite Questions-in-Context | Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber | EMNLP 2019 |
| Complex Factoid Question Answering with a Free-Text Knowledge Graph | Chen Zhao, Chenyan Xiong, Xin Qian, Jordan Boyd-Graber | WWW 2020 |
Principal Investigator
Jordan Boyd-Graber — University of Maryland, College Park.
Research interests: machine learning, probabilistic graphical models, and their applications in natural language processing.
Research interests: machine learning, probabilistic graphical models, and their applications in natural language processing.