To capture such informative features, we then map the semantic role tag of each word to a real-valued vector x sr of dimension d sr using a embedding matrix SR ∈ R dsr×|Vsr| , where V sr is the set of semantic role label vocabulary.
we compare the performances between the models adding semantic role information in embedding layer and those only using the word embeddings.