Conference paper Open Access

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

Nicos Isaak; Loizos Michael


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    <subfield code="a">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 and under Grant Agreement No 823783 and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</subfield>
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    <subfield code="a">WinoReg: A New Faster and More Accurate Metric of Hardness for Winograd Schemas</subfield>
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    <subfield code="a">&lt;p&gt;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&lt;br&gt;
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&lt;br&gt;
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&lt;br&gt;
work, making its use as part of other WSC-based systems feasible.&lt;/p&gt;</subfield>
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