Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences. In this paper, we tackle two key challenges in ESC: enhancing contextually relevant and empathetic response generation through dynamic demonstration retrieval, and advancing cognitive understanding to grasp implicit mental states comprehensively. We introduce Dynamic Demonstration Retrieval and Cognitive-Aspect Situation Understanding (\ourwork), a novel approach that synergizes these elements to improve the quality of support provided in ESCs. By leveraging in-context learning and persona information, we introduce an innovative retrieval mechanism that selects informative and personalized demonstration pairs. We also propose a cognitive understanding module that utilizes four cognitive relationships from the ATOMIC knowledge source to deepen situational awareness of help-seekers' mental states. Our supportive decoder integrates information from diverse knowledge sources, underpinning response generation that is both empathetic and cognitively aware. The effectiveness of \ourwork is demonstrated through extensive automatic and human evaluations, revealing substantial improvements over numerous state-of-the-art models, with up to 13.79% enhancement in overall performance of ten metrics. Our codes are available for public access to facilitate further research and development.
Our code (in codes) mainly references https://github.com/chengjl19/PAL/tree/main
You should first download the BlenderBot-small model and put the pytorch_model.bin file in Blenderbot_small-90M.
You should then download the dpr-reader-single-nq-base model and put the pytorch_model.bin file in DPR.
Then run _reformat/pre_process.py
And run _reformat/process.py
And run process.py
Next run RUN/prepare_strat.sh
And run RUN/train_strat.sh