Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning (2024)

Abstract: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterances in unannotated conversations, this task that has garnered increasing attention recently. Previous methods often apply Emotion-Cause Pair Extraction (ECPE) task models, treating the entire conversation as a whole for contextual interaction. However, statistical analysis shows that the number of emotion-cause pairs in ECPEC conversation data far exceeds that in ECPE datasets, leading to interference among multiple events within a conversation and causing noise to propagate between different events. To address this issue, we propose a novel CEnter eveNT-guided framEwoRk (CENTER). This model introduces a Center Event Detection task to construct a center event-aware graph that captures the unique representations of different event regions. Additionally, mimicking human reasoning processes, we build a center event reasoning graph and use graph neural network to facilitate the flow of information between utterance pairs, thereby uncovering the relationships between emotions and their causes. Experimental results demonstrate that our approach achieves state-of-the-art performance across three benchmark datasets.

## introduction with the development of news media, individuals can rapidly express and disseminate their opinions through social platforms. by analyzing the emotion polarity and intensity in conversation data @xcite , it is possible to comprehend the intent of the text and uncover hidden information @xcite . given that the causes of emotions are key elements for in-depth emotion understanding, the emotion-cause pair extraction (ecpec) task @xcite has garnered significant attention. this task involves extracting all potential emotion utterances and their corresponding causes from unannotated conversations, thereby gaining insights into the emotion propagation between speakers and comprehensively understanding their emotion interactions. ecpec can be applied to various practical scenarios, such as public opinion monitoring @xcite , social media information detection @xcite , marketing @xcite , and psychological interventions @xcite . in the ecpec task, accurately analyzing the causes of emotions is challenging due to the unique semantic properties of conversational content, such as context dependencies, complex syntactic structures, and interactivity @xcite . the main approaches to addressing these chal-lenges can be categorized into sequential encoding and aggregated encoding: (1) sequential encoding: this method focuses on learning and enhancing context based on the sequential order of utterances in a conversation. @xcite employ cross-attention mechanisms to learn the contextual dependencies and interactivity between utterances, effectively extracting emotion and cause utterances. @xcite incorporate speaker features as supplementary information, sequentially concatenating these with utterances to enrich the representations. (2) aggregated encoding: this approach uses connection graph to learn the syntactic structure of conversations through graph convolutional network (gcn). @xcite use fully connected networks to represent the relationships between utterances and employ gcns to capture the syntactic dependencies between them. @xcite transform the task into a machine reading comprehension problem, encoding utterances based on the positional relationships between different speakers within the conversation. while current research address the unique semantic challenges present in conversation content, existing methods treat conversation as a single semantic unit, employing mechanisms such as graph neural networks, attention mechanisms, and recurrent neural networks for contextual enhancement. this approach can lead to the intermingling of information in lengthy conversations, resulting in counterfactual reasoning where causes appear after emotions. inspired by embodied cognition mentioned @xcite , we observe that conversation participants may exhibit various emotions and associated causes around certain center utterances, with conversation typically exhibiting a discrete multi-event structure. specifically, as illustrated in figure 1 , utterances u 1 and u 2 reflect speaker b's frustration over speaker a not getting the job, while u 6 and u 8 reflect speaker b's happiness about actions taken to relieve stress. if we do not differentiate between different centers of conversation content and instead encode the entire conversation as a whole for context learning, noise from unrelated content will increase the difficulty for the model to understand these scenes, making it challenging to accurately capture the intrinsic connections between emotions and their causes. in light of the above considerations, we propose a novel center event-guided framework (cen-ter) for the ecpec task. this method identifies center events within conversations and traces the causes of emotions from the perspective of human cognitive reasoning through center-aware graphs and center reasoning graphs. specifically, we first define the concept of center events and introduce a new ecpec auxiliary task called center event detection (ced). besides, we construct a conversation center event-aware graph from the predicted center sequence to facilitate feature aggregation within different center events. additionally, recognizing the importance of center events in conversations, we emphasize their uniqueness in the pairing process by establishing a candidate pair center event reasoning graph, which promotes information flow between features of pairs. finally, we employ an end-to-end multi-task framework to mitigate interference between different events and enhance the efficient flow of multi-granularity features. experiments conduct on three ecpec benchmark datasets demonstrate that center achieves state-of-the-art performance. our contributions are summarized as follows: ## task definition in the ecpec task, the input data format consists of three hierarchical levels, i.e., "conversationutterance-word". the input includes a series of and each utterance consists of m words u @xmath0, w 2 , • • • , w m }. the main objective of the ecpec task is to extract all ec pairs from the conversation in d, which is described as: nodes and non-center ones for utterance-level feature enhancement; (3) center event reasoning (cer) graph, which is constructed by center pairs and non-center pairs for reasoning between emotion and cause; (4) conversation emotion-cause extraction, which integrates multi-dimensional feature representations and uses a multi-task framework for final classification. ## conclusion in this paper, we propose a novel center eventguided framework (center) for emotion-cause pair extraction in conversation. this framework detects center events within conversations and constructs a center event-aware network and a center event inference network. by enhancing the centrality of utterance and utterance pair representations, it mitigates the interference between different events and effectively captures the relationships between emotions and their corresponding causes. we evaluate our approach using the ecpe-d-dd, ecpe-d-ie, and convecpe datasets, demonstrating that center outperforms other models in the ecpec task.