We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the
word. Our focus is on developing principled theoretical approaches which are grounded in linguistic phenomena, leading to more explainable models. We show that translations from multiple languages can be leveraged to improve the accuracy on the WiC task.
