DROP (Discrete Reasoning Over Paragraphs)

Introduced by Dua et al. in DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Discrete Reasoning Over Paragraphs DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets. The questions consist of passages extracted from Wikipedia articles. The dataset is split into a training set of about 77,000 questions, a development set of around 9,500 questions and a hidden test set similar in size to the development set.

Source: https://allennlp.org/drop


Paper Code Results Date Stars


Similar Datasets