ECSGen and iZen: A New NLP Task and a Zeroshot Framework to Perform it

In recent years, there has been a renewed interest in Emotion-Cause Analysis (ECA) related Natural Language Processing (NLP) tasks. Most of these are classification tasks in nature with little to no emphasis on the rapidly advancing field of text generation with machine learning models. In this paper, we propose a new generative task within the ECA domain named ECSGen (Emotion-Cause Mitigating Suggestion Generation). The task is to generate relevant suggestions to mitigate the cause of a negative emotion expressed in a given text. We propose iZen, a technical framework that leverages large language model (LLM) to perform this task in a zero-shot manner without requiring any new training or fine-tuning steps. We curate two new datasets to evaluate iZen's ability to perform the ECSGen task. Our experiments and analysis demonstrate iZen's promising performance in the ECSGen task.