In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function.
Question: How do they combine text representations with the knowledge graph embeddings?
all three representations are concatenated and passed into a MLP