lowed to extract each of them is described below: Text Features: We represent the textual utterances in the dataset using BERT (Devlin et al., 2018), which provides a sentence representation u t ∈ R dt for every utterance u.
we average the last four transformer layers of the first token ([CLS]) in the utterance -using the BERT-Base model -to get a unique utterance representation of size d t = 768.