ECFCON: Emotion Consequence Forecasting in Conversations

Xincheng Ju, Dong Zhang, Suyang Zhu, Junhui Li, Shoushan Li, Guodong Zhou

Abstract:

Conversation is a common form of human communication that includes extensive emotional interaction.
Traditional approaches focused on studying emotions and their underlying causes in conversations.
They try to address two issues: what emotions are present in the dialogue and what causes these emotions.
However, these works often overlook the bidirectional nature of emotional interaction in dialogue:
utterances can evoke emotionscause, and emotions can also lead to certain utterances consequence.
Therefore, we propose a new issue: what consequences arise from these emotions?
This leads to the introduction of a new task called Emotion Consequence Forecasting in CONversations (ECFCON).
In this work, we first propose a corresponding dialogue-level dataset.
Specifically, we select 2,780 video dialogues for annotation, totaling 39,950 utterances. Out of these, 12,391 utterances contain emotions, and 8,810 of these have discernible consequences.
Then, we benchmark this task by conducting experiments from the perspectives of traditional methods,
generalized LLMs prompting methods, and clue-driven hybrid methods. Both our dataset and benchmark codes are openly accessible to the public.

Primary Subject Area: [Engagement] Emotional and Social Signals

Relevance To Conference: This work proposes a new task named Emotion Consequence Forecasting in CONversations (ECFCON), accompanied by a corresponding dialogue-level dataset. In addition, we have designed experiments from the perspectives of traditional methods, generalized LLMs prompting methods, and clue-driven hybrid methods. We hope that our work can help analyze the consequences of emotions in multimodal conversational videos.

Submission Number: 3836