Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning

Authors Suk Joon Hong, Brandon Bennett

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Suk Joon Hong
  • School of Mathematics, University of Leeds, UK
  • InfoMining Co., Seoul, South Korea
Brandon Bennett
  • School of Computing, University of Leeds, UK

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Suk Joon Hong and Brandon Bennett. Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 41:1-41:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


The Winograd Schema Challenge (WSC) is a commonsense reasoning task that requires background knowledge. In this paper, we contribute to tackling WSC in four ways. Firstly, we suggest a keyword method to define a restricted domain where distinctive high-level semantic patterns can be found. A thanking domain was defined by keywords, and the data set in this domain is used in our experiments. Secondly, we develop a high-level knowledge-based reasoning method using semantic roles which is based on the method of Sharma [Sharma, 2019]. Thirdly, we propose an ensemble method to combine knowledge-based reasoning and machine learning which shows the best performance in our experiments. As a machine learning method, we used Bidirectional Encoder Representations from Transformers (BERT) [Jacob Devlin et al., 2018; Vid Kocijan et al., 2019]. Lastly, in terms of evaluation, we suggest a "robust" accuracy measurement by modifying that of Trichelair et al. [Trichelair et al., 2018]. As with their switching method, we evaluate a model by considering its performance on trivial variants of each sentence in the test set.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Commonsense Reasoning
  • Winograd Schema Challenge
  • Knowledge-based Reasoning
  • Machine Learning
  • Semantics


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