206 citations · 277 across the 18 of their papers we have counts for
3 papers · 1 filter
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan +3
A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time consuming to obtain. However, it has been shown that…
SemEval-2021 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS)
Nancy X. R. Wang, Diwakar Mahajan, Marina Danilevsky +1
Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this p…
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Wang, Ban Kawas +3
Data scientists face a steep learning curve in understanding a new domain for which they want to build machine learning (ML) models. While input from domain experts could offer val…