The Winograd Schema Challenge (WSC) is a common-sense 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 key-words, 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 [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) [Kocijan et al., 2019]. Lastly, in terms of evaluation, we suggest a "robust" accuracy measurement by modifying that of 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.

中文翻译：

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通过基于知识的推理和机器学习来解决特定领域的Winograd模式

Winograd模式挑战（WSC）是常识性推理任务，需要背景知识。在本文中，我们将以四种方式为解决WSC做出贡献。首先，我们建议使用一种关键字方法来定义一个可以在其中找到独特的高级语义模式的受限域。一个感谢的领域是由关键字定义的，并且在我们的实验中使用了该领域中的数据集。其次，我们基于Sharma [2019]的方法，开发了一种基于语义角色的高级知识推理方法。第三，我们提出了一种将基于知识的推理与机器学习相结合的集成方法，该方法在我们的实验中表现出最好的性能。作为一种机器学习方法，我们使用了来自变压器的双向编码器表示（BERT）[Kocijan等人，2019]。最后，在评估方面，我们建议“