JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity

Abstract

There is a growing research interest in studying word similarity. Without a doubt, two similar words in a context may be considered different in another context. Therefore, this paper investigates the effect of the context in word similarity. The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity). In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish, and Slovenian. Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pre-trained embeddings techniques for each of the four languages. Our proposed model outperformed the baseline models in both subtasks and achieved the best result for subtask 2 in English and Finnish, with a score of 0.725 and 0.68, respectively. We have been ranked the sixth in subtask 1, with English, Croatian, Finnish, and Slovenian with scores as follows: 0.738, 0.44, 0.546, 0.512.