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.

通过对话语境和反馈响应进行对比学习，本文研究了将其嵌入到相同表示空间的可能性。通过使用嵌入模型评估其在美式英语对话中作为上下文 - 反馈适当性度量和反馈响应排序的效果，结果表明该模型在相同排序任务上优于人类，且学习到的嵌入包含了反馈响应的会话功能信息。