  Short feedback responses, such as backchannels, play an important role in
spoken dialogue. So far, most of the modeling of feedback responses has focused
on their timing, often neglecting how their lexical and prosodic form influence
their contextual appropriateness and conversational function. In this paper, we
investigate the possibility of embedding short dialogue contexts and feedback
responses in the same representation space using a contrastive learning
objective. In our evaluation, we primarily focus on how such embeddings can be
used as a context-feedback appropriateness metric and thus for feedback
response ranking in U.S. English dialogues. Our results show that the model
outperforms humans given the same ranking task and that the learned embeddings
carry information about the conversational function of feedback responses.
