Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations

Anonymous

Abstract: 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 2340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification and zero-shot and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora.

Paper Type: long

Research Area: Discourse and Pragmatics

Contribution Types: Approaches to low-resource settings, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English