1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2022
Collecting high-quality adversarial data for machine reading comprehension tasks with humans and models in the loop
Damian Y. Romero Diaz, Magdalena Anioł, John Culnan
We present our experience as annotators in the creation of high-quality, adversarial machine-reading-comprehension data for extractive QA for Task 1 of the First Workshop on Dynami…
cs.CL2019★ 1 cited
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Hannah Smith, Zeyu Zhang, John Culnan +1
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present…