Multi-step Prompting for Few-shot Emotion-Grounded Conversations
Mauajama Firdaus, Gopendra Vikram Singh, Asif Ekbal, Pushpak Bhattacharyya · 2023
Conversational systems have shown immense growth in their ability to communicate like humans. With the emergence of large pre-trained language models (PLMs) the ability to provide informative responses have improved significantly. Despite the success of PLMs, the ability to identify and generate engaging and empathetic responses is largely dependent on labelled-data. In this work, we design a prompting approach that identifies the emotion of a given utterance and uses the emotion information for generating the appropriate responses for conversational systems. We propose a two-step prompting method that first recognises the emotion in the dialogue utterance and in the second-step uses the predicted emotion to prompt the PLM to generate the corresponding em- pathetic response in a few-shot setting. Experimental results on three publicly available datasets show that our proposed approach outperforms the state-of-the-art approaches for both automatic and manual evaluation.