@inproceedings{prakash-etal-2019-combining,
    title = "Combining Knowledge Hunting and Neural Language Models to Solve the {W}inograd Schema Challenge",
    author = "Prakash, Ashok  and
      Sharma, Arpit  and
      Mitra, Arindam  and
      Baral, Chitta",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-1614/",
    doi = "10.18653/v1/P19-1614",
    pages = "6110--6119",
    abstract = "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."
}