When reading the Tensorflow tutorial for Word Embeddings, I found two notes that confuse me:
Note: Experimentally, you may be able to produce more interpretable embeddings by using a simpler model. Try deleting the Dense(16) layer, retraining the model, and visualizing the embeddings again.
Note: Typically, a much larger dataset is needed to train more interpretable word embeddings. This tutorial uses a small IMDb dataset for the purpose of demonstration.
I don't know exactly what it means by "more interpretable" in those two notes, is it related to the result displayed by the embedding projector? And also why the interpretability will increase when reducing model's complexity?