Extracting Emotion-Cause Pairs from Multimodal Conversations: A Robust Multi-Stage Framework

핵심 개념
A multi-stage framework is proposed to effectively extract emotion-cause pairs from multimodal conversational data, achieving state-of-the-art performance on the SemEval-2024 Task 3 competition.
The paper presents a multi-stage framework for extracting emotion-cause pairs from multimodal conversational data. In the first stage, the authors utilize the Llama-2-based InstructERC model to extract the emotion category of each utterance in a conversation. This is a crucial step, as accurately identifying the emotional states of utterances is key to the subsequent causal pair extraction. For the causal pair extraction, the authors employ a two-stream attention model (TSAM) that considers both speaker and emotion information to predict the causal utterance given the target emotion. To further enhance performance, the authors incorporate emotion prediction as an auxiliary task within a multi-task learning framework. Additionally, the authors leverage multimodal information, including audio and video features, to enrich the context and improve the overall emotion analysis capabilities of the models. Specifically, they extract audio features using openSMILE and generate video descriptions using large language models like LLaVA and Video-LLaMA. The proposed framework achieved first place in both subtasks of the SemEval-2024 Task 3 competition, demonstrating its effectiveness in extracting emotion-cause pairs from multimodal conversational data.
"Comprehending emotions plays a vital role in developing artificial intelligence with human-like capabilities, as emotions are inherent to humans and exert a substantial impact on our thinking, choices, and social engagements." "Going beyond simple emotion identification, unraveling the underlying catalysts of these emotions within conversations represents a more complex and less-explored challenge."
"Motivated by the phenomenon that the performance of the emotion recognition of utterances in a conversation harnessed by the traditional manner is generally poor, we design a new pipeline framework." "Our approach achieved first place for both of the two subtasks in the competition."

더 깊은 문의

How can the proposed multi-stage framework be extended to handle more complex conversational scenarios, such as multi-party interactions or conversations with interruptions and topic shifts?

To extend the proposed multi-stage framework for handling more complex conversational scenarios, such as multi-party interactions or conversations with interruptions and topic shifts, several enhancements can be considered: Multi-party Interaction Handling: Introduce a speaker identification module to differentiate between speakers in multi-party conversations. Implement a speaker-aware attention mechanism to capture the contributions of each speaker to the conversation. Develop a graph-based model to represent the relationships and interactions between multiple speakers and their utterances. Conversations with Interruptions and Topic Shifts: Incorporate a dialogue segmentation component to identify different segments within a conversation. Utilize a context-aware model that can adapt to topic shifts and interruptions by maintaining context across different segments. Implement a memory mechanism to store and retrieve relevant information from previous segments to maintain coherence in the conversation. Dynamic Context Management: Develop a dynamic context management system that can adapt to changes in the conversation structure. Implement a mechanism to handle interruptions by pausing the current analysis and resuming from the point of interruption. Introduce a topic tracking module to detect and adapt to shifts in conversation topics. By incorporating these enhancements, the multi-stage framework can effectively handle more complex conversational scenarios with multiple parties, interruptions, and topic shifts, enabling more robust and context-aware emotion and causal analysis.