Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts (2023)

Abstract: Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents.ECPE is an important task for developing human-like responses. However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues. To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information).PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement. We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles. We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion. For reproducing the results, we make available all our code and data.

## introduction with increased interest in developing human-like responses, it is crucial to determine the cause of a given emotion. as part of such interest, there is a surge of research activities that analyze the cause of emotions @xcite @xcite . recently, @xcite presents reccon, a new dataset for emotion cause extraction (ece) task in dialogue. ece is a task to find a clause that contains the cause of an annotated emotion in a clause of a given document. however, ece is limited in that the model requires manually annotated emotions. to overcome the limitation of ece @xcite , @xcite suggest emotion cause pair extraction (ecpe) task, which automatically predicts emotion clauses in a given document and 1 https://github.com/jdjin3000/prg-moe # of emotion-cause pairs ecpe-news @xcite ecpe-d 1 1,746 (89.77%) 9 (0.80%) 2 177 (9.10%) 19 (1.69%) ≥ 3 22 (1.13%) 1,094 (97.50%) in this paper, we propose a pair-relationship guided mixture-of-experts (prg-moe) model, which considers dialogue features (e.g., speaker information) in ecpe. we employ mixture-of-experts (moe) @xcite to customize the experts in the relationship between utterances. it is important to consider the relationship between utterances, since it helps us grasp the emotional flow from the conversation history or understand emotions (or its causes) through other speakers. prg-moe automatically learns the relationship between utterances and advises gating networks to incorporate dialogue features, which yields excellent performance. we evaluate prg-moe and other models using ecpe-d dataset and show that prg-moe outperforms other models. furthermore, we propose a multi-class classification task that identifies cause types of emotioncause pairs in a dialogue -ecpe-ct. an understanding of the cause types is beneficial especially for empathetic response generation @xcite . ecpe-ct helps to generate more specific empathetic responses rather than simple reactions for all kinds of context such as "good luck" by understanding several cause types. knowing the cause types of the last utterance by the user can help the agent to comprehend the context and obtain the implicit feedback from the user. there are three cause types in ecpe-d: no-context, interpersonal, and self-contagion. type is categorized depending on 1) into from which speaker the cause of the emotion originated, and 2) whether the cause appears in the current utterance or not. we performed ecpe-ct tasks under various models; prg-moe outperformed other baselines in identifying not only emotion-cause pairs but also types of causes in the pairs. our contributions are as follows: ## task definition this section describes the definition of ecpe task in a dialogue. the input is a dialogue @xmath0, ..., u n } that contains multiple utterances between two people, where u i is i-th utterance in a dialogue and consists of token sequence t i , speaker indicator s @xmath1, 1} and emotion information e i . objective of the task is to extract a set of emotion-cause utterance pairs {..., (u i , u j ), ...}, where u i is an emotion utterance and u j is a cause utterance in a pair. ## pair-relationship guided mixture-of-experts we propose a pair-relationship guided mixture-of-experts (prg-moe) model that adopts a mixtureof-experts module. figure 2 shows the overall architecture. prg-moe consists of three modules: utterance representation construction ( §4.1), emotion-cause pair candidate extraction ( §4.2) and mixture-of-experts based emotion-cause pair classification ( §4.3). ## cause type classification in ecpe this section describes the experiments and results of classifying the cause type of emotions -ecpe-ct. the cause type in ecpe-ct is categorized based on from which speaker the cause is generated and where the cause is found in the paired utterances. ecpe-ct enhances understanding of the cause of the emotion, enabling more effective use of the cause. for example, most chatbots used in different settings generate responses based on the found cause @xcite . however, ecpe-ct could assist to generate more empathetic responses by incorporating other cases, as categorized below. cause types in ecpe-d are as follows: we test prg-moe and ecpe-2d, which have best performances among the baselines. for multiclass classification, we modify the output layer of models to be able to output multi-class prediction. ## conclusion in this paper, we present prg-moe, a novel approach for extracting emotion-cause pairs from a dialogue by considering speaker and emotion information. we guide the mixture-of-experts module to consider the relationship between utterances in pairs. to guide mixture-of-experts, we define pairrelationship, which is the relationship between utterances. we combine the decision of the gating network and pair-relationship information for routing the emotion-cause pairs to proper experts. we also propose a new task, ecpe-ct, which classifies emotion-cause pair by cause type. we evaluate the task with ecpe-d, a dialogue dataset with more emotion-cause pairs than other benchmark ecpe datasets. with ecpe-d, we show that prg-moe outperforms other ecpe models in ecpe and multi-class ecpe tasks.