activity
20172021
most citedFrame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold

95 citations · 118 across the 4 of their papers we have counts for

collaborators

14 papers

cs.CL2021

Sister Help: Data Augmentation for Frame-Semantic Role Labeling

Ayush Pancholy, Miriam R. L. Petruck, Swabha Swayamdipta

While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its la…

cs.CL202111 cited

DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts

Alisa Liu, Maarten Sap, Ximing Lu +4

Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time…

cs.CL2021

Contrastive Explanations for Model Interpretability

Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel +3

Contrastive explanations clarify why an event occurred in contrast to another. They are more inherently intuitive to humans to both produce and comprehend. We propose a methodology…

cs.CL202112 cited

Challenges in Automated Debiasing for Toxic Language Detection

Xuhui Zhou, Maarten Sap, Swabha Swayamdipta +2

Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investiga…

cs.CL2020

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie +4

Large datasets have become commonplace in NLP research. However, the increased emphasis on data quantity has made it challenging to assess the quality of data. We introduce Data Ma…

cs.CL2020

The Right Tool for the Job: Matching Model and Instance Complexities

Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta +2

As NLP models become larger, executing a trained model requires significant computational resources incurring monetary and environmental costs. To better respect a given inference…