Just to add to @Abhilash_Srivastava post: Even if you used pretrained word embeddings you can decide to keep the fixed or to (further) train them. Keeping them fixed obviously increases training speed. Training them, however, might make them more adapt to our context, potentially yielding a higher accuracy.
Language is extremely multifaceted and different embedding approaches make different assumptions to map words into a vector space – that’s why we have so many embedding solutions in the first place. For example, the Skip-gram model behind Word2Vec considers to words/vectors similar, if they are used in the same context. Under this assumption, “ugly” and “beautiful” are rather close in the embedding space since words words are used to describe appearances. Thus, Word2Vec doesn’t directly capture the notion of polarity what is important for, e.g., sentiment analysis.
In short, even when using pretrained embeddings, try both: keeping them fixed vs. training them, and see what comes out of it.
Regarding your plot – although that’s pure anecdotal speculation – I don’t think that this is an embedding problem. Pretrained embeddings typically improve things but are now completely magic either. What about the first test one can do: Can you overtrain the network in a small sample of your dataset?