Cheap Facts and Counter-Facts
Rui Wang, Chris Callison-Burch · 2010
This paper describes our experiments of us-ing Amazon’s Mechanical Turk to generate (counter-)facts from texts for certain named-entities. We give the human annotators a para-graph of text and a highlighted named-entity. They will write down several (counter-)facts about this named-entity in that context. The analysis of the results is performed by com-paring the acquired data with the recognizing textual entailment (RTE) challenge dataset. 1