E - CORE : Emotion Correlation Enhanced Empathetic Dialogue Generation (2023)

Abstract: Achieving empathy is a crucial step toward humanized dialogue systems. Current approaches for empathetic dialogue generation mainly perceive an emotional label to generate an empathetic response conditioned on it, which simply treat emotions independently, but ignore the intrinsic emotion correlation in dialogues, resulting in inaccurate emotion perception and unsuitable response generation. In this paper, we propose a novel emotion correlation enhanced empathetic dialogue generation framework, which comprehensively realizes emotion correlation learning, utilization, and supervising. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions from different resolutions, further modeling emotion correlation. Then we propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy, respectively improving the emotion perception and response generation. Experimental results on the benchmark dataset demonstrate the superiority of our model in both empathetic perception and expression.

## introduction empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities, helping to achieve a humanized dialogue system @xcite . empathetic dialogue generation (empdg) which aims at perceiving the emotional expressions in dialogue to generate appropriate responses rich in empathy, is proposed and has attracted extensive attention with its ability to improve user experience and satisfaction in multiple domains @xcite . most existing methods follow a multi-task learning paradigm, jointly training an emotional classification task and dialogue generation task to achieve response generation with empathetic constraints. recent works take their effort on two aspects. the first focuses on improving the emotion perception, for example, by introducing external knowledge @xcite @xcite , mining emotion causes @xcite , or more fine-grained emotion modeling @xcite . the other focuses on promoting the generation strategy, based on mixture of experts @xcite , different emotion look-ahead reward functions @xcite , emotional mimicry @xcite and so on. in general, these methods first perform mainemotion prediction with a single-label emotional classifier, then inject the predicted emotion into generation to achieve empathetic expression. the above paradigm implicitly introduces an independent assumption on different emotions, both in modeling and utilization, which respectively from the learning for maximizing separation between different emotions in classification and the abandonment of secondary emotions in generation. however, studies on social psychology @xcite suggest that human emotions are not completely independent, but with an intrinsic correlation, manifested as dialogues and responses are typically accompanied with the co-occurrence of multiple emotions @xcite . this independent assumption with ignoring the emotion correlation directly impairs main-emotion perception, as the main-emotion as a whole feature should be codetermined by the occurred emotions in context. (fig. 1-a , the common correlation weights of grateful and terrified helps distinguish the true emotions afraid). moreover, this assumption is also harmful for response generation, as the model dominated by one emotion lacks the ability to recognize emotional transitions (fig. 1 , a transition from afraid for accident to grateful for survival), resulting in unsuitable responses (fig. 1-b , only "sorry to hear" for survival). therefore, considering the emotion correlation is necessary for precise emotion perception and better empathetic expression. statistical result 1 on benchmark dataset in fig. 2 , which is calculated based on the quantity of other-emotionrelated words in samples, further suggests that the emotion correlation learning is significant for em-pdg task with a proportion of samples containing secondary emotions reaches 84.04%. as the annotation for all subtle emotions in dialogues is hard and inefficiency, we propose to mine and incorporate this intrinsic emotion correlation into single-labeled empdg. there are three challenges: 1) modeling and learning the multiemotion correlation; 2) utilizing the correlated co-occurrence emotions without biasing toward to the labeled emotion; 3) providing supervision to avoid excessive or erroneous introduction of multiemotion information. our contributions are summarized as follows: 1) we propose breaking the emotion independence assumption existing in current methods and modeling the intrinsic emotion correlation. to the best of our knowledge, this is one of the first frameworks in empdg that explicitly models and utilizes emotion correlation to enhance emotion perception and response generation. 2) we propose a distinctive method with three tailored modules respectively addressing the emotion correlation learning, utilizing, and supervising, which effectively and accurately capture the correlated co-occurrence emotions in dialogues even under single-label, enhancing empathy perception and expression. 3) extensive experiments verify the superiority of our method on both emotion prediction (8.34% in accuracy) and response generation (8.53% in perplexity). ablation studies and specialized experiments on constructed multi-emotion annotated sub-dataset also validates the fidelity of our emotion correlation learning. ## proposed approach given a dialogue context @xmath0, u 2 , . . . , u m ] of m utterances, empathetic dialogue generation aims to generate the next empathetic response y with emotional consistency and informative expression. optionally, the task performs emotion prediction based on context semantic understanding to achieve empathetic constraints. in this section, we give a detailed introduction to our proposed e-core, which explicitly mines and incorporates the emotion correlation to enhance empathetic perception and expression. the framework consists of 3 phases: context encoding, multi-resolution emotion graph network, and emotion correlation enhanced decoding, as is illustrated in fig. 3 . ## results and analysis we conduct experiments on the benchmark dataset to verify the promise of emotion correlation learning in both emotion perception and empathetic generation. then we investigate the ability of co-occurrence emotions recognition on the multiemotion annotated subset, to further validate the essence of emotion correlation learning in our method e-core. ## conclusion in this paper, we propose to exploit the intrinsic emotion correlation in dialogues to enhance empathetic dialogue generation. a distinctive framework with three effective modules respectively addressing the emotion correlation learning, utilizing, and supervising, is designed. extensive experiments on the benchmark dataset prove the significant advantages of our framework in improving emotion perception and empathetic generation. specific analysis further demonstrates the accuracy of our emotion correlation learning. in the future, our work can inspire other approaches to explore emotionrelated tasks with multi-emotion correlation learning, without being limited by single-emotion label. limitations 1) firstly, as we analyzed in the introduction, almost all dialogues are accompanied by subtle emotions besides the main-emotion. however, it is almost impossible to annotate all subtle emotions and even the emotion weights for a dialogue. although our method based on emotion correlation modeling has effectively achieved multi-emotion learning for empdg under single-label guidance, how to improve the network to utilize existing in-formation to provide more effective supervision for multi-emotion learning still needs to be considered. this is also a common problem faced by many emotion-related generation tasks. the ablation study on the model without response reconstruction loss supervision shown in appendix j indicates that the supervision for multi-emotion partly sources from the empathetic response, which may serve as an improved inspiration. 2) secondly, all existing methods are evaluated on the unique benchmark dataset empatheticdialogues @xcite . as empathetic dialogue generation is an emerging task, only one relevant english dataset has been proposed, lacking of datasets in more languages and categories for reference. 3) finally, we observed in the experiment that the existing models tend to generate generic responses, especially for complex hard samples, which are difficult to capture the key points. therefore, the learning of hard samples is also a developing direction of the empathetic dialogue generation task.