Effect of embedding dimension in performance of embedding algorithms
You are probably just overfitting. 

When you increase the number of parameters that are being trained in your model, the likelihood of them learning some aspect of the training data that doesn't generalize to unseen data increases. So there should be some sweet spot, where the model is able to express the underlying structure in the data sufficiently, without putting too much emphasis on those aspects that are specific to the training data.