Published March 14, 2021 | Version Accepted pre-print
Conference paper Open

Blending NLP and Machine Learning for the Development of Winograd Schemas

  • 1. Open University of CyprusNicosiaCyprus
  • 2. Open University of Cyprus Nicosia Cyprus and CYENS Center of Excellence Nicosia Cyprus

Description

The Winograd Schema Challenge (WSC), a novel litmus test for machine intelligence, has been proposed to advance the field of AI. Over the last decade, AI researchers have become increasingly interested in this challenge. While a common and trivial task for humans, studies have shown that the WSC is still difficult for current AI systems. Tackling the challenge would likely require
access to a sufficiently rich set of Winograd schema examples, which are currently limited in their number and too cumbersome to create completely manually. Towards addressing these limitations, we propose a machine-driven approach for the development of large numbers of schemas. Our empirical evaluation suggests that our developed system, which blends the advantages of Machine
Learning and Natural Language Processing, is able to automatically develop Winograd schemas autonomously, or considerably help humans in the development task.

Notes

This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

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Additional details

Funding

RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission