Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge.The needed knowledge is not present in the given text.Automatic extraction of the needed knowledge is a bottleneck in solving the challenge.The existing state-of-the-art approach uses the knowledge embedded in their pretrained language model.However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts.This limits the performance of such models on the WSC problems.In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module.Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively.Overall our system achieves the state-of-theart accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.

Abstract The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.