  This paper describes the system submitted by our team (BabelEnconding) to
SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word
Similarity. We propose an approach that relies on translation and multilingual
language models in order to compute the contextual similarity between pairs of
words. Our hypothesis is that evidence from additional languages can leverage
the correlation with the human generated scores. BabelEnconding was applied to
both subtasks and ranked among the top-3 in six out of eight task/language
combinations and was the highest scoring system three times.
