Why does fixing the pretrained word embedding give lower accuracy than training from scratch? I was doing multi-class text classification task and I built 2 models, one LSTM model that trains from scratch and other LSTM model with fixed pretrained word embedding.
Fixing the pretrained word embedding gives lower accuracy than training from scratch on both the validation and training data.
What is the reason behind that? Shouldn't the pretrained word give better word representation?

I am not sure what you mean by "Fixing the pretrained word embedding", but you are supposed to refine the pre-trained model by training it on your data. This pre-trained model is just supposed to be a good initialization.
However, even if you refine the pre-trained model, the result is often worse than a model trained from scratch.
Pretraining is mainly used if you don't have sufficient data and/or resources to train a large model yourself. Otherwise, the model trained from scratch is often better. It just means that the population the pre-trained model was trained on is too different from your population.
