Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
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 pre-trained 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-the-art accuracy of 71.06{\%} on the WSC dataset, an improvement of 7.36{\%} over the previous best.
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WSC
QA-SRL
