Definitely not. What you are doing is not fine-tuning, you are adding more tokens to the existing vocabulary. Those added tokens will be randomly initialized and not contain any meaningful representation. The language model was never taught how those words need to be represented. If on top of this you also tune the model on a word-only task, then it might work.
What I meant was having a fine-tune task with a large corpus where you specifically have on word as input in RoBERTa and some expected output. I would not know which tasks you could use for this, but theoretically it could work. But you would need to finetune the model, which you do not seem to want to do.
Again: in your case it might be a better idea to stick with content-free representations. BERT and friends are not intended for context-free representations.