A Human-Machine Collaboration Framework for the Development of Schemas

Abstract

The Winograd Schema Challenge (WSC), a seemingly well-thought-out test for machine intelligence, has been proposed to shed light on developing systems that exhibit human behavior. Since its introduction, it aimed to pivot the focus of the AI community from the technology to the science of AI. While common and trivial for humans, studies show that it is still challenging for machines, especially when they have to deal with novel schemas, that is, well-designed sentences that require the resolving of definite pronouns. As researchers have become increasingly interested in the challenge itself, this presumably necessitates the availability of an extensive collection of Winograd schemas, which goes beyond what human experts can reasonably develop themselves, especially after proposed ways of utilizing them as novel forms of CAPTCHAs. To address this necessity, we propose a novel framework that explicitly focuses on how humans and machines can collaborate as teammates to design novel schemas from scratch. This is being accomplished by combining two recent studies from the literature: i) Winventor, a machine-driven approach for the development of large amounts of Winograd schemas, albeit not of high quality, and ii) WinoFlexi, an online crowdsourcing system that allows crowd workers to develop a limited number of schemas often of similar quality to that of experts. Our proposal crafts a new road map toward developing a novel collaborative platform that amplifies human and machine intelligence by combining their complementary strengths.