E mp H i: Generating Empathetic Responses with Human-like Intents (2022)

Abstract: In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy. To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi for short. Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents. Experiments show that EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation. Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.

## introduction empathy is a basic yet essential human ability in our daily life. it is a capacity to show one's caring and understanding to others. many types of research have been conducted on empathetic expression to enhance the empathy ability of artificial intelligence, e.g., computational approach for empathy measurement @xcite , empathetic expression understanding in newswire @xcite , online mental health support @xcite , etc. in this work, we focus on the task of generating empathetic responses @xcite @xcite in open-domain conversation. existing empathetic dialogue models pay more attention to the emotion-dependent response generation @xcite . i am sorry to hear that . i hope you find someone to help you. that is so annoying. i would be upset too. ## empathetic expression bias although existing empathetic conversational methods have shown promising progress, we reveal there is a severe bias of empathetic expression between them and humans according to quantitative evaluation. empathy plays a vital role in human conversation, welivita and pu (2020) make a taxonomy of empathetic intents and calculate the frequency of each intent in empatheticdialogues @xcite . as shown in figure 2 , humans show i love my friend, she just drove me to class. context encoder emotion classifier intent keywords response decoder wow! you should do something back for her! vocabulary prior network recognition network intent predictor context + + copy gate response 𝜶 1-𝜶 𝑲𝑳(𝒑|𝒒) emotion intent implicit gate context attention their empathy naturally by questioning, acknowledging, and agreeing intents etc. however, there are no empirical experiments have shown how empathetic dialogue models express their empathy? to further study, we finetune a bert classifier @xcite on the released empatheticintents foot_0 dataset @xcite . our classifier achieves 87.75% accuracy in intent classification and we apply it to identify the empathetic intents of responses generated by the state-of-the-art empathetic dialogue model mime @xcite . as shown in figure 4 , the severe empathetic intent distribution bias emerges while comparing humans to mime. given context with sad emotion, existing models usually generate "i am sorry to hear that" with sympathiz-ing intent, which is not human-like and contextually relevant. in addition, we can tell that the empathetic expression of mime is monotonous. we also quantify the intent distribution of other empathetic dialogue models in the appendix a. the results are similar to figure 4 . we believe this phenomenon is caused by that existing models only generate responses according to context emotion and lack fine-grained empathetic intent modeling. therefore, we propose emphi, which generates empathetic responses with humanlike empathetic intents. emphi learns the potential empathetic intent distribution with a latent variable z, which could be seen in figure 5 . conditional variational autoencoder (cvae) @xcite @xcite is trained to maximize the conditional log likelihood, log p(x|c), which involves an intractable marginalization over z. we train the cvae efficiently with stochastic gradient variational bayes (sgvb) (kingma and welling, 2014) by maximizing the variational lower bound of the log likelihood: p(x|c, z) denotes response reconstruction probability, q(z|x, c) is recognition probability and p(z|c) is prior probability. our method mainly consists of three aspects: ## conclusion overall, we reveal the severe bias of empathetic expression between existing dialogue models and humans. to address this issue, this paper proposes emphi to generate empathetic responses with human-like empathetic intents. as a result, both automatic and human evaluation prove that emphi has a huge improvement on empathetic conversation. according to the anlaysis and case studies, emphi successfully learns the emapthetic intent distribution of human and shows high interpretability and controllability during the generation process. we will try large pretrained language models with empathetic intent in our future work.