What is the "semantic level"? I am reading the paper Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction (2018) by Yunzhe Tao et al.

In this paper, they use several times the expression "semantic levels". Some examples:


HRHN can adaptively select the relevant exogenous features in different semantic levels
the temporal information is usually complicated and may occur at different semantic levels
The encoder RHN reads the convolved features $(w_1,w_2,···,w_{T−1})$ and models their temporal dependencies at different semantic levels
Then an RHN is used to model the temporal dependencies among convolved input features at different semantic levels


What is the semantic level?
In language theory, there are generally several admitted levels that can be studied in relation with one another or independently. The semantic level is the one dealing with the meaning of the text ("semantic" comes from the greek and means "to signify"). The semantic level is therefore generally independent from the syntax and even the language used to convey the message. 
Here is an interesting picture I found on the internet to illustrate my point.


EDIT: I took some time reading the paper. I think "semantic levels" refers here to the different neural networks layers used for the exogenous features. 

Here is a modified version of their figure I've drawned to make it clearer:


In particular, from what I have understood so far, the attention coefficients apply to the whole semantic level (which I find not really clearly indicated on their figure)

The LHS of their figure would then be better described by this new one:



Hope this helps!