My research focuses on studying biases in datasets and models. Good biases, such as structural inductive biases help language understanding - check out my PhD thesis on these. But biases can be undesirable, e.g. spurious correlations commonly found in crowd-sourced, large-scale datasets due to annotation artifacts, or social prejudices of human annotators and task designers, which are difficult to rid!.
I obtained my PhD from Carnegie Mellon University in May 2019, where I was advised by Noah Smith and Chris Dyer. During most of my PhD I was a visiting student at the University of Washington in Seattle.
Update I am looking for academic positions in Winter / Spring 2021!
|Mar 1, 2021||Check out our new pre-print on contrastive explanations for model decisions! Work with my intern Alon Jacovi and others!|
|Feb 24, 2021||Talk at the NERT Seminar at Georgetown University! So honored to be an elected speaker :)|
|Feb 12, 2021||Invited talk at the NLP Seminar at Georgia Tech!|
|Feb 3, 2021||Check out our new pre-print on an evaluation metric for open-ended text generation, MAUVE !|
|Feb 1, 2021||New ACL submission on controlled generation, with exciting applications. Keep an eye out!|
|Jan 11, 2021||Paper on Challenges in Social Bias Mitigation in Hate Speech Detection to appear at EACL 2021!|
|Dec 3, 2020||Guest lecture in Eunsol Choi’s Topics in NLP class at UT Austin on Biases and Interpretability.|
|Nov 2, 2020||Was delighted to be an invited speaker for Responsible AI at the Microsoft E+D Product Leaders Conference.|
|Sep 15, 2020||Paper on Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics is now accepted to the Proceedings of EMNLP, and GDaug is accepted to Findings of EMNLP.|
|Aug 13, 2020||Completed one year as a postdoctoral investigator at AI2!|