Why increasing the number of units or layers does not increase the accuracy and decrease the loss?
The title of your question and the question itself are somehow different but I'll try to answer the question, the meaning of decreasing loss without changes in accuracy.

The reason is simply due to using probabilities. For instance, for classification task if you have an output $0.7$ for input and the last layer is a softmax, then you classify the input as that class which has $0.7$. Imagine you train more and that output changes to something like $0.95$. Consequently, the accuracy does not change because you already classify it as what it really is but the loss lessens. 

To answer the question which is in the body of your post, there can be numerous reasons that I'll try to refer to them. 

One of the possibilities is that your data of different classes have overlap in the current feature space. This may lead to high Bayes error. For instance, suppose you have two same inputs and the label of them are contradictory. In this situation, your performance cannot be improved. To check it whether you've got this problem or not, take a look at the histogram of your data. 

Another problem can be the weakness of LSTM networks which cannot memorise numerous things. LSTM models are very good at things like considering the gender of a subject or the plural or singular form of subjects but in cases where they should consider many things simultaneously, they have difficulties.

Another reason can be the incorrect way of using dropout. At first, do not use it and let your network overfits the training data to find a good model. After fitting your data, try to use dropout. 

You can also test Stacked LSTMs which are powerful models.