2 citations · 3 across the 3 of their papers we have counts for
4 papers
Exploring the Influence of Label Aggregation on Minority Voices: Implications for Dataset Bias and Model Training
Mugdha Pandya, Nafise Sadat Moosavi, Diana Maynard
Resolving disagreement in manual annotation typically consists of removing unreliable annotators and using a label aggregation strategy such as majority vote or expert opinion to r…
Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?
Dominic Petrak, Nafise Sadat Moosavi, Ye Tian +2
Learning from free-text human feedback is essential for dialog systems, but annotated data is scarce and usually covers only a small fraction of error types known in conversational…
FERMAT: An Alternative to Accuracy for Numerical Reasoning
Jasivan Alex Sivakumar, Nafise Sadat Moosavi
While pre-trained language models achieve impressive performance on various NLP benchmarks, they still struggle with tasks that require numerical reasoning. Recent advances in impr…
Lessons Learned from a Citizen Science Project for Natural Language Processing
Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe +6
Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is…