  We present a generalizable classification approach that leverages Large
Language Models (LLMs) to facilitate the detection of implicitly encoded social
meaning in conversations. We design a multi-faceted prompt to extract a textual
explanation of the reasoning that connects visible cues to underlying social
meanings. These extracted explanations or rationales serve as augmentations to
the conversational text to facilitate dialogue understanding and transfer. Our
empirical results over 2,340 experimental settings demonstrate the significant
positive impact of adding these rationales. Our findings hold true for
in-domain classification, zero-shot, and few-shot domain transfer for two
different social meaning detection tasks, each spanning two different corpora.
