> One "word in context" task is to look at 2 different sentences that have a common word and decide if that word means the same thing in both sentences or different things (more details here: https://pilehvar.github.io/wic/)

Can anyone explain what makes this difficult for a machine? What existing knowledge does the machine start with? At a glance, it doesn't feel like it should be difficult if the machine had a large corpus to train on that showed many examples of each words in different contexts.