ProDepDet: Out-of-domain Knowledge Transfer of Pre-trained Large Language Models for Depression Detection in Text-Based Multi-Party Conversations

Parameter-efficient, soft, and prompt-based tuning methods have received increasing attention in various downstream tasks due to the high cost of traditional fine-tuning methods in pre-trained language models (PLM). Prompt tuning (PT) is one such effective mechanism which has achieved remarkable performance in transferring the acquired knowledge of a PLM to perform an unseen task within the same domain using task-specific prompts and informative instructions. Even though most prior work considers in-domain knowledge transfer using PT, much work remains to be done for PT-based out-of-domain knowledge transfer. In this study we propose ProDepDet, a novel framework specifically designed to use a PLM's knowledge about structure and semantic modelling in multi-party conversations to perform the unseen, out-of-domain task of depression detection. To our knowledge, this study is the first attempt to adapt the acquired knowledge of a PLM for out-of-domain task modelling using PT-based cross-task transferability. Experiments on few-shot and full data settings across multiple benchmark datasets demonstrate the superiority of our PT framework in two downstream tasks including depressed utterance classification and depressed speaker identification.