WinoReg: A New Faster and More Accurate Metric of Hardness for Winograd Schemas

The Winograd Schema Challenge (WSC), the task of resolving pronouns in certain carefully-structured sentences, has received considerable interest in the past few years as an alternative to the Turing Test. In our recent work we demonstrated the plausibility of
using commonsense knowledge, automatically acquired from raw text in English Wikipedia, towards computing a metric of hardness for a limited number of Winograd Schemas. In this work we present WinoReg, a new system to compute hardness of Winograd
Schemas, by training a Random Forest classier over a rich set of features identied in relevant WSC works in the literature. Our empirical study shows that this new system is considerably faster and more accurate compared to the system proposed in our earlier
work, making its use as part of other WSC-based systems feasible.