Tackling Domain-Specific Winograd Schemas with Knowledge-Based Reasoning and Machine Learning
This work provides an incremental approach to common-sense reasoning for domain-specific WSC tasks, potentially benefiting researchers working on targeted natural language understanding problems.
This paper addresses the Winograd Schema Challenge (WSC) within a specific 'thanking' domain, identifying distinctive semantic patterns. It proposes an ensemble method combining knowledge-based reasoning and BERT, which achieved the best performance in their experiments.
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.