A Review of Winograd Schema Challenge Datasets and Approaches

TL;DR

This paper reviews existing datasets and approaches for the Winograd Schema Challenge, a benchmark for evaluating commonsense reasoning and natural language understanding in AI systems.

Contribution

It provides a comprehensive overview of the datasets and methods developed for the Winograd Schema Challenge since its inception.

Findings

Summarizes key datasets used in the challenge

Analyzes various approaches and their effectiveness

Highlights gaps and future directions in research

Abstract

The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be resolved correctly. The examples were designed to be easily solvable by humans but difficult for machines, in principle requiring a deep understanding of the content of the text and the situation it describes. This paper reviews existing Winograd Schema Challenge benchmark datasets and approaches that have been published since its introduction.