MIME : MIM icking Emotions for Empathetic Response Generation (2020)

Abstract: Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME .

## introduction empathy is a fundamental human trait that reflects our ability to understand and reflect the thoughts and feelings of the people we interact with. in the social sciences, research on empathy has evolved into an entire field of study, addressing the social underpinning of empathy @xcite , the cognitive and emotion aspects of empathy @xcite , and its connection to personal and demographic traits @xcite @xcite . the study of empathy has found a wide range of applications in healthcare, including psychotherapy @xcite or more broadly as a mechanism to improve the quality of care @xcite . computational models of empathy have been proposed only in recent years, partly because of the complexity of this behavior which makes it difficult to emulate with computational approaches. in natural language processing, the methods proposed to date address the tasks of understanding expressions of empathy in newswire @xcite , counseling conversations @xcite , or generating empathy in dialogue @xcite . work has also been done on the construction of empathy lexicons @xcite or large empathy dialogue datasets @xcite . in this paper, we address the task of generating empathetic responses that mimic the emotion of the speaker while accounting for their affective charge (positive or negative). we adopt the idea of emotion mixture, as the state-of-the-art moel @xcite , to achieve the appropriate balance of emotions in positive and negative emotion groups. however, inspired by @xcite , we introduce stochasticity into the mixture at emotiongroup level for varied responses. this becomes particularly important in cases where the input utterance can be responded with ambivalent, yet befitting utterances. fig. 1 shows one such example where the response to a positive utterance is ambivalent. the paper makes two important contributions. first, it introduces a new approach for empathetic generation that encodes context and emotions, and uses emotion stochastic sampling and emotion mimicry to generate responses that are appropriate and empathetic for positive or negative statements. we show that this approach leads to performance exceeding the state-of-the-art when trained and evaluated on a large empathy dialogue dataset. second, through extensive feature ablation experiments, we shed light on the role played by emotion mimicry and emotion grouping for the task of empathetic response generation. ## related work open domain conversational models have made good progress in recent years @xcite @xcite . many of them can generate persona-consistent @xcite and diverse @xcite responses, but those are not necessarily empathetic. producing empathetic responses requires apt handling of emotions and sentiments @xcite @xcite . @xcite model psychological concepts as memory states in lstm @xcite and employ emotion-category embeddings in the decoding process. @xcite presents a gan @xcite based framework with emotion-specific generators. on a larger scale, @xcite use the emojis in twitter posts as emotion labels and introduce an attention-based @xcite seq-to-seq @xcite model with conditional variational autoencoder @xcite for emotional response generation. however, they only produce affective responses with userprovided emotion, which may not necessarily be empathetic to the speakers. @xcite introduce a dual-decoder network to generate responses with given sentiment (positive or negative). @xcite formulate a reinforcement learning problem to maximize user's sentimental feeling towards the generated response. @xcite present an encoder-decoder model with each emotion having a dedicated decoder. variational bayes @xcite has been widely adopted into natural language generation tasks @xcite and successfully extended to dialog generation tasks @xcite .the prominent approach by hierarchical encoder-decoder (vhred) @xcite integrates vae with the sequence-to-sequence decoder based on markov assumptions. ## methodology our model mime is based on the assumption that empathetic responses often mimic the emotion of the speaker @xcite -in our case, the human subject or user. for example, positivelycharged utterances are usually responded with positive emotions, although they can also be ambivalent as illustrated in fig. 1 . on the other hand, responding to negatively-charged utterances often requires composite emotions that agree with the user's emotion, but also tries to comfort them with some positivity, such as hopefulness or silver lining. as such, we strive to balance the mimicry of context/user emotion during empathetic response generation. to this end, we first obtain context representation using a transformer encoder architecture @xcite . similar to the state-of-the-art (sota) model moel @xcite , we enforce emotion understanding in the context representation by classifying user emotion during training. for the response emotion, we first group the 32 emotions into two groups containing positive and negative emotions (section 3.3). next, a probability distribution of emotions is sampled for each of these groups that corresponds to the emotion of the response. positive and negative response emotion representations are formed from these distributions and emotion embeddings. these two representations are appropriately combined to balance the two kinds of emotions to form the emotional representation that drives the emotional state during response generation using transformer decoder @xcite . fig. 2 shows the architecture of our model. ## experimental settings during inference, we use the emotion classifier (eq. ( 5 )) with emotion grouping ( ## conclusion this paper introduced a novel empathetic generation strategy that relies on two key ideas: emotion grouping and emotion mimicry. also, stochasticity was applied to the emotion mixture for varied response generation. we have shown through several human evaluations and ablation studies that our model is better equipped for empathetic response generation than existing models. however, there remains much room for improvement, particularly in terms of fluency where our model falters. moreover, emotions like 'surprise' and 'anticipation' might be explicitly dealt with due to their ambiguous polarity.