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?