Girish Kumar — accepted AI-conference papers

2023

Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari +3

ACL 2023long

Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to…

Cited by 97 Source ↗PDF ↗code ↗Scholar ↗

2021

TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling

Parker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh

ACL 2021long

We present a novel approach to the problem of text style transfer. Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at infer…

Cited by 62 Source ↗PDF ↗Scholar ↗