165 citations · 420 across the 25 of their papers we have counts for
7 papers · 1 filter
Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages
Jiao Sun, Tongshuang Wu, Yue Jiang +3
People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raisi…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts
Tongshuang Wu, Michael Terry, Carrie J. Cai
Although large language models (LLMs) have demonstrated impressive potential on simple tasks, their breadth of scope, lack of transparency, and insufficient controllability can mak…
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Bingsheng Yao, Dakuo Wang, Tongshuang Wu +4
Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions th…
DeHumor: Visual Analytics for Decomposing Humor
Xingbo Wang, Yao Ming, Tongshuang Wu +3
Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate voc…
Tailor: Generating and Perturbing Text with Semantic Controls
Alexis Ross, Tongshuang Wu, Hao Peng +2
Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, whic…
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer +1
While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only…