A Review of Winograd Schema Challenge Datasets and Approaches — mentioned by Ice_cream_suit

Tue, 30 Mar 2021 07:20:59 GMT

The abstract approach to AI is still around and fruitful although it may not be fad of the day among the Phronix crowd. For example look at a recent paper by Ernest Davis, the author of the review linked to above: https://arxiv.org/pdf/2004.13831.pdf A Review of Winograd Schema Challenge Datasets and Approaches Vid Kocijan1∗ , Thomas Lukasiewicz1,2 , Ernest Davis3 , Gary Marcus4 and Leora Morgenstern5 1University of Oxford 2Alan Turing Institute, London 3New York University 4Robust AI 5Systems & Technology Research / PARC firstname.lastname@cs.ox.ac.uk, davise@cs.nyu.edu, gary.marcus@nyu.edu…

Paper and discussion history. A mention is not an endorsement.

A Review of Winograd Schema Challenge Datasets and Approaches — mentioned by square_usual

Thu, 04 Jun 2020 09:20:18 GMT

> The breakthrough didn’t crack 80%, so three cheers for wide credibility intervals with error margin, but I expect the predictor might be feeling slightly more nervous now with one year left to go. Good call on that by the author; according to this summary paper [1], a model reached ~79-93% accuracy on various Winograd data sets. [1] https://arxiv.org/pdf/2004.13831.pdf

Paper and discussion history. A mention is not an endorsement.

A Review of Winograd Schema Challenge Datasets and Approaches · HackerPapers discussions

A Review of Winograd Schema Challenge Datasets and Approaches — mentioned by Ice_cream_suit

A Review of Winograd Schema Challenge Datasets and Approaches — mentioned by square_usual