161 citations · 656 across the 43 of their papers we have counts for
13 papers · 1 filter
Learning from Task Descriptions
Orion Weller, Nicholas Lourie, Matt Gardner +1
Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an exam…
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions
James Ferguson, Matt Gardner, Hannaneh Hajishirzi +2
Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the conte…
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics
Anthony Chen, Gabriel Stanovsky, Sameer Singh +1
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, pro…
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Sanjay Subramanian, Lucy Lu Wang, Sachin Mehta +6
Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several su…
Improving Compositional Generalization in Semantic Parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta +2
Generalization of models to out-of-distribution (OOD) data has captured tremendous attention recently. Specifically, compositional generalization, i.e., whether a model generalizes…
Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq
Qiang Ning, Hao Wu, Pradeep Dasigi +5
High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designin…